subject:( Time series analysis)

Rutgers University

1. Yan, Xi, 1985-. Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization.

Degree: PhD, Statistics and Biostatistics, 2019, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/62066/

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This dissertation focuses on developing new statistical methods for analyzing and modeling financial time series. The first part of this dissertation discusses modeling of functional… (more)

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This dissertation focuses on developing new statistical methods for analyzing and modeling financial time series. The first part of this dissertation discusses modeling of functional and distributional time series, assuming the series are driven by a finite dimensional underlying feature process. Functional time series are commonly observed in finance and difficult to model mainly due to high dimensionality. We instead focus on a low-dimensional latent feature process and connect it with the original functional time series through generalized state-space models. A state-space model assumes that observations are driven by an underlying dynamic state process and is widely used in many fields. We propose a generalized two-stage Sequential Monte Carlo (SMC) joint estimation framework to model functional time series driven by its feature process through state-space models and perform on-line estimations and predictions. In order to improve computation efficiency, we also implement parallel computing and re-design computation algorithms to integrate with non-linear optimization and SMC calculations. Two financial applications are presented to demonstrate the robustness and efficiency of our proposed framework. The first application aims to extract and model the daily implied risk neutral densities from observed call option prices. We view the underlying risk neutral density as a functional time series driven by its feature process and model it with a parametric mixed log-normal distribution through a state-space model. We conduct both simulation and empirical studies and compare prediction performance of our models with that of random walk models. Empirically, the proposed models improves prediction performance significantly. The second financial application studies daily cross-sectional distribution of 1000 largest market capitalization stock returns from year 1991 to year 2002. Similarly we view cross-sectional distribution as a functional time series driven by its feature process and model it with a four-parameter generalized skewed t-distribution. Using proposed two-stage SMC joint estimation framework, we build models separately for different market conditions, including the dot-com crisis. In both bearish and bullish markets, prediction performances of our models gain substantial improvement comparing with random walk models. The second part of dissertation presents a new portfolio optimization strategy for minimum variance portfolios with constraints on short-sale and transaction costs. Unlike traditional mean-variance theory, our method minimizes only the portfolio risk and uses analysts' consensus ratings to pre-screen stocks. An empirical study of S&P 500 stocks from year 1990 to year 2009 is conducted to demonstrate effectiveness. We show that portfolios constructed and optimized using our strategy deliver considerable improvement of performance in terms of Sharpe ratio comparing with benchmark portfolios and portfolios in past literatures.

Advisors/Committee Members: Chen, Rong (chair), Xiao, Han (internal member), Tan, Zhiqiang (internal member), Lin, Ming (outside member), School of Graduate Studies.

Subjects/Keywords: Functional time series; Time-series analysis

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APA (6^th Edition):

Yan, Xi, 1. (2019). Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62066/

Chicago Manual of Style (16^th Edition):

Yan, Xi, 1985-. “Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization.” 2019. Doctoral Dissertation, Rutgers University. Accessed April 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/62066/.

MLA Handbook (7^th Edition):

Yan, Xi, 1985-. “Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization.” 2019. Web. 16 Apr 2021.

Vancouver:

Yan, Xi 1. Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Apr 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62066/.

Council of Science Editors:

Yan, Xi 1. Statistical analysis of dynamic risk neutral density, dynamic cross-sectional distribution and portfolio optimization. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62066/

University of Johannesburg

2. Human, Johannes Urbanus. Some aspects of harmonic time series analysis.

Degree: PhD, 2012, University of Johannesburg

URL: http://hdl.handle.net/10210/4275

► Harmonic time series are often used to describe the periodic nature of a time series, for example the periodic nature of a variable star’s observed… (more)

Subjects/Keywords: Harmonic analysis; Time-series analysis

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APA (6^th Edition):

Human, J. U. (2012). Some aspects of harmonic time series analysis. (Doctoral Dissertation). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/4275

Chicago Manual of Style (16^th Edition):

Human, Johannes Urbanus. “Some aspects of harmonic time series analysis.” 2012. Doctoral Dissertation, University of Johannesburg. Accessed April 16, 2021. http://hdl.handle.net/10210/4275.

MLA Handbook (7^th Edition):

Human, Johannes Urbanus. “Some aspects of harmonic time series analysis.” 2012. Web. 16 Apr 2021.

Vancouver:

Human JU. Some aspects of harmonic time series analysis. [Internet] [Doctoral dissertation]. University of Johannesburg; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10210/4275.

Council of Science Editors:

Human JU. Some aspects of harmonic time series analysis. [Doctoral Dissertation]. University of Johannesburg; 2012. Available from: http://hdl.handle.net/10210/4275

The Ohio State University

3. Michel, Jonathan R. Essays in Nonlinear Time Series Analysis.

Degree: PhD, Economics, 2019, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555001297904158

► This dissertation consists of six papers. Each of these papers are on a different aspect of statistical analysis of nonlinear time series. In the first… (more)

Subjects/Keywords: Economics; Time Series Analysis, Nonstationary time series, Mixing, Nonlinear Time Series

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APA (6^th Edition):

Michel, J. R. (2019). Essays in Nonlinear Time Series Analysis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1555001297904158

Chicago Manual of Style (16^th Edition):

Michel, Jonathan R. “Essays in Nonlinear Time Series Analysis.” 2019. Doctoral Dissertation, The Ohio State University. Accessed April 16, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555001297904158.

MLA Handbook (7^th Edition):

Michel, Jonathan R. “Essays in Nonlinear Time Series Analysis.” 2019. Web. 16 Apr 2021.

Vancouver:

Michel JR. Essays in Nonlinear Time Series Analysis. [Internet] [Doctoral dissertation]. The Ohio State University; 2019. [cited 2021 Apr 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555001297904158.

Council of Science Editors:

Michel JR. Essays in Nonlinear Time Series Analysis. [Doctoral Dissertation]. The Ohio State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1555001297904158

Nelson Mandela Metropolitan University

4. Mlambo, Farai Fredric. Good's casualty for time series: a regime-switching framework.

Degree: Faculty of Science, 2014, Nelson Mandela Metropolitan University

URL: http://hdl.handle.net/10948/6018

► Causal analysis is a significant role-playing field in the applied sciences such as statistics, econometrics, and technometrics. Particularly, probability-raising models have warranted significant research interest.… (more)

Subjects/Keywords: Time-series analysis; Econometrics

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APA (6^th Edition):

Mlambo, F. F. (2014). Good's casualty for time series: a regime-switching framework. (Thesis). Nelson Mandela Metropolitan University. Retrieved from http://hdl.handle.net/10948/6018

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mlambo, Farai Fredric. “Good's casualty for time series: a regime-switching framework.” 2014. Thesis, Nelson Mandela Metropolitan University. Accessed April 16, 2021. http://hdl.handle.net/10948/6018.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mlambo, Farai Fredric. “Good's casualty for time series: a regime-switching framework.” 2014. Web. 16 Apr 2021.

Vancouver:

Mlambo FF. Good's casualty for time series: a regime-switching framework. [Internet] [Thesis]. Nelson Mandela Metropolitan University; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10948/6018.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mlambo FF. Good's casualty for time series: a regime-switching framework. [Thesis]. Nelson Mandela Metropolitan University; 2014. Available from: http://hdl.handle.net/10948/6018

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

5. Slaughter, Justin Michael. Small-sample prediction of estimated loss potentials.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/24496

► This thesis constructs predictions for the 2003 General Liability premises and operations estimated loss potentials (ELPs) for Manufacturers and Contractors (MC) and Owners, Landlords, and… (more)

Subjects/Keywords: Actuarial; Bootstrapping; Time-Series Analysis

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APA (6^th Edition):

Slaughter, J. M. (2014). Small-sample prediction of estimated loss potentials. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/24496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Slaughter, Justin Michael. “Small-sample prediction of estimated loss potentials.” 2014. Thesis, University of Georgia. Accessed April 16, 2021. http://hdl.handle.net/10724/24496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Slaughter, Justin Michael. “Small-sample prediction of estimated loss potentials.” 2014. Web. 16 Apr 2021.

Vancouver:

Slaughter JM. Small-sample prediction of estimated loss potentials. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10724/24496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Slaughter JM. Small-sample prediction of estimated loss potentials. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/24496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Queens University

6. Rahim, Karim. Applications of Multitaper Spectral Analysis to Nonstationary Data .

Degree: Mathematics and Statistics, 2014, Queens University

URL: http://hdl.handle.net/1974/12584

► This thesis is concerned with changes in the spectrum over time observed in Holocene climate data as recorded in the Burgundy grape harvest date series.… (more)

▼ This thesis is concerned with changes in the spectrum over time observed in Holocene climate data as recorded in the Burgundy grape harvest date series. These changes represent nonstationarities, and while spectral estimation techniques are relatively robust in the presence of nonstationarity – that is, they are able to detect significant contributions to power at a given frequency in cases where the contribution to power at that given frequency is not constant over time – estimation and prediction can be improved by considering nonstationarity. We propose improving spectral estimation by considering such changes. Specifically, we propose estimating the level of change in frequency over time, detecting change-point(s) and sectioning the time series into stationary segments. We focus on locating a change in frequency domain in time, and propose a graphical technique to detect spectral changes over time. We test the estimation technique in simulation, and then apply it to the Burgundy grape harvest date series. The Burgundy grape harvest date series was selected to demonstrate the introduced estimator and methodology because the time series is equally spaced, has few missing values, and a multitaper spectral analysis, which the methodology proposed in this thesis is based on, of the grape harvest date series was recently published. In addition, we propose a method using a test for goodness-of-fit of autoregressive estimators to aid in assessment of change in spectral properties over time. This thesis has four components: (1) introduction and study of a level-of-change estimator for use in the frequency domain change-point detection, (2) spectral analysis of the Burgundy grape harvest date series, (3) goodness-of-fit estimates for autoregressive processes, and (4) introduction of a statistical software package for multitaper spectral analysis. We present four results. (1) We introduce and demonstrate the feasibility of a level-of-change estimator. (2) We present a spectral analysis and coherence study of the Burgundy grape harvest date series that includes locating a change-point. (3) We present a study showing an advantage using multitaper spectral estimates when calculating autocorrelation coefficients. And (4) we introduce an R software package, available on the CRAN, to perform multitaper spectral estimation.

Subjects/Keywords: Spectral Analysis ; Time Series

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APA (6^th Edition):

Rahim, K. (2014). Applications of Multitaper Spectral Analysis to Nonstationary Data . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/12584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rahim, Karim. “Applications of Multitaper Spectral Analysis to Nonstationary Data .” 2014. Thesis, Queens University. Accessed April 16, 2021. http://hdl.handle.net/1974/12584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rahim, Karim. “Applications of Multitaper Spectral Analysis to Nonstationary Data .” 2014. Web. 16 Apr 2021.

Vancouver:

Rahim K. Applications of Multitaper Spectral Analysis to Nonstationary Data . [Internet] [Thesis]. Queens University; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1974/12584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahim K. Applications of Multitaper Spectral Analysis to Nonstationary Data . [Thesis]. Queens University; 2014. Available from: http://hdl.handle.net/1974/12584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Hong Kong University of Science and Technology

7. Wu, Degang. Coupling analysis in time series using information theory and dynamical systems theory.

Degree: 2016, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-87522 ; https://doi.org/10.14711/thesis-b1736171 ; http://repository.ust.hk/ir/bitstream/1783.1-87522/1/th_redirect.html

► Inferring causality from observations of different entities is central to science. Time series is an important form of observations in subjects ranging from physics, geology… (more)

Subjects/Keywords: Time-series analysis ; Mathematical models

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APA (6^th Edition):

Wu, D. (2016). Coupling analysis in time series using information theory and dynamical systems theory. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-87522 ; https://doi.org/10.14711/thesis-b1736171 ; http://repository.ust.hk/ir/bitstream/1783.1-87522/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wu, Degang. “Coupling analysis in time series using information theory and dynamical systems theory.” 2016. Thesis, Hong Kong University of Science and Technology. Accessed April 16, 2021. http://repository.ust.hk/ir/Record/1783.1-87522 ; https://doi.org/10.14711/thesis-b1736171 ; http://repository.ust.hk/ir/bitstream/1783.1-87522/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wu, Degang. “Coupling analysis in time series using information theory and dynamical systems theory.” 2016. Web. 16 Apr 2021.

Vancouver:

Wu D. Coupling analysis in time series using information theory and dynamical systems theory. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2016. [cited 2021 Apr 16]. Available from: http://repository.ust.hk/ir/Record/1783.1-87522 ; https://doi.org/10.14711/thesis-b1736171 ; http://repository.ust.hk/ir/bitstream/1783.1-87522/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu D. Coupling analysis in time series using information theory and dynamical systems theory. [Thesis]. Hong Kong University of Science and Technology; 2016. Available from: http://repository.ust.hk/ir/Record/1783.1-87522 ; https://doi.org/10.14711/thesis-b1736171 ; http://repository.ust.hk/ir/bitstream/1783.1-87522/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

8. Klop, Stijn (author). From observation well to model area: Estimating groundwater levels spatially using time series analysis.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:362f6b0f-b3aa-41c6-b47e-b625c963d8a0

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Estimating groundwater levels spatially using time series analysis. With time series analysis a response function of an observation well is determined. The response function is… (more)

Subjects/Keywords: Groundwater; time series analysis

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APA (6^th Edition):

Klop, S. (. (2019). From observation well to model area: Estimating groundwater levels spatially using time series analysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:362f6b0f-b3aa-41c6-b47e-b625c963d8a0

Chicago Manual of Style (16^th Edition):

Klop, Stijn (author). “From observation well to model area: Estimating groundwater levels spatially using time series analysis.” 2019. Masters Thesis, Delft University of Technology. Accessed April 16, 2021. http://resolver.tudelft.nl/uuid:362f6b0f-b3aa-41c6-b47e-b625c963d8a0.

MLA Handbook (7^th Edition):

Klop, Stijn (author). “From observation well to model area: Estimating groundwater levels spatially using time series analysis.” 2019. Web. 16 Apr 2021.

Vancouver:

Klop S(. From observation well to model area: Estimating groundwater levels spatially using time series analysis. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 16]. Available from: http://resolver.tudelft.nl/uuid:362f6b0f-b3aa-41c6-b47e-b625c963d8a0.

Council of Science Editors:

Klop S(. From observation well to model area: Estimating groundwater levels spatially using time series analysis. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:362f6b0f-b3aa-41c6-b47e-b625c963d8a0

Rutgers University

9. Liu, Xialu, 1986-. New models and methods for time series analysis in big data era.

Degree: PhD, Statistics and Biostatistics, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/47468/

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In big data era, available information becomes massive and complex and is often observed over time. Conventional time series models are limited in capability of… (more)

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In big data era, available information becomes massive and complex and is often observed over time. Conventional time series models are limited in capability of dealing with these type of data. This dissertation focuses on developing new statistical models, along with their associated estimation procedures, to analyze time series data in functional form, and in high dimension, with linear or nonlinear dynamics, which can be broadly applicable to finance, environment, engineering, biological and medical sciences. Functional data analysis has became an increasingly popular class of problems in statistical research. However, functional data observed over time with serial dependence remains a less studied area. Motivated by Bosq (2000), who worst introduced the functional autoregressive (FAR) models, we propose a convolutional functional autoregressive (CFAR) model, where the function at time t is a result of the sum of convolutions of the past functions with a set of convolution functions, plus a noise process, mimicking the autoregressive process. It provides an intuitive and direct interpretation of the dynamics of a stochastic process. We adopt a sieve estimation procedure based on the B-spline approximation of the convolution functions. We establish convergence rate of the proposed estimator, and investigate its theoretical properties. The model building, model validation, and prediction procedures are also developed. As for high-dimensional time series data, dimension reduction is an important issue and can be effectively performed by factor analysis. Considering the factor impacts may vary under different conditions, we propose a factor model with regime-switching mechanism, allowing loadings to change across regimes, and combined eigendecomposition and Viterbi algorithm for estimation. We discover that, with multiple states of different 'strength', the convergence rate of loading matrix estimator for strong states is the same as the one-regime case, while the rate improves for weak states, gaining extra information from strong states. The theoretical properties of the procedure are investigated as well. In addition, we propose a new class of nonparametric seasonal time series models under the framework of the functional coefficient model. The coefficients in the proposed model change over time and consist of the trend and seasonal components to characterize seasonality. A local linear approach is developed to estimate the nonparametric trend and seasonal effect functions. The proposed methodologies are illustrated by two simulated examples and the model is applied to characterizing the seasonality of the monthly number of tourists visiting Hawaii.

Advisors/Committee Members: Chen, Rong (chair), Xiao, Han (internal member), Dicker, Lee (internal member), Lin, Xiaodong (outside member).

Subjects/Keywords: Time-series analysis; Big data

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APA (6^th Edition):

Liu, Xialu, 1. (2015). New models and methods for time series analysis in big data era. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47468/

Chicago Manual of Style (16^th Edition):

Liu, Xialu, 1986-. “New models and methods for time series analysis in big data era.” 2015. Doctoral Dissertation, Rutgers University. Accessed April 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/47468/.

MLA Handbook (7^th Edition):

Liu, Xialu, 1986-. “New models and methods for time series analysis in big data era.” 2015. Web. 16 Apr 2021.

Vancouver:

Liu, Xialu 1. New models and methods for time series analysis in big data era. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2021 Apr 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47468/.

Council of Science Editors:

Liu, Xialu 1. New models and methods for time series analysis in big data era. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47468/

Rutgers University

10. Chang, Kun. Topics in compositional, seasonal and spatial-temporal time series.

Degree: PhD, Statistics and Biostatistics, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/48411/

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This dissertation studies several topics in time series modeling. The discussion on seasonal time series, compositional time series and spatial-temporal time series brings new insight… (more)

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This dissertation studies several topics in time series modeling. The discussion on seasonal time series, compositional time series and spatial-temporal time series brings new insight to the existing methods. Innovative methodologies are developed for modeling and forecasting purposes. These topics are not isolated but to naturally support each other under rigorous discussions. A variety of real examples are presented from economics, social science and geoscience areas. The second chapter introduces a new class of seasonal time series models, treating the seasonality as a stable composition through time. With the objective of forecasting the sum of the next ell observations, the concept of rolling season is adopted and a structure of rolling conditional distribution is formulated under the compositional time series framework. The probabilistic properties, the estimation and prediction, and the forecasting performance of the model are studied and demonstrated with simulation and real examples. The third chapter focuses on the discussion of compositional time series theories in multivariate models. It provides an idea to the modeling procedure of the multivariate time series that has sum constraints at each time. It also proposes a joint MLE method for threshold vector-error correction models. This chapter interprets the methodologies with an real example of the U.S. household consumption expenditures data. Threshold cointegration effects are analyzed on the U.S. real GDP growth rate. The estimation of TVECM is compared by the current two-step estimation method and the proposed joint MLE approach. Sensor allocation problem is studied in Chapter 4 under spatial-temporal models in Gaussian random fields. Given observations from existing sensors, the problem is solved by minimizing the integrated conditional variance based on different forecasting purposes. In this chapter, the time series are measured at different locations with both spatial and time series correlations. The spatial-temporal covariance structure is extensively discussed under both separable and nonseparable cases. The model is finally applied to the ozone level measurements in Harris County, Texas.

Advisors/Committee Members: Chen, Rong (chair), Xie, Minge (internal member), Xiao, Han (internal member), Cheng, Jerry (outside member).

Subjects/Keywords: Time-series analysis; Prediction theory

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APA (6^th Edition):

Chang, K. (2015). Topics in compositional, seasonal and spatial-temporal time series. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48411/

Chicago Manual of Style (16^th Edition):

Chang, Kun. “Topics in compositional, seasonal and spatial-temporal time series.” 2015. Doctoral Dissertation, Rutgers University. Accessed April 16, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/48411/.

MLA Handbook (7^th Edition):

Chang, Kun. “Topics in compositional, seasonal and spatial-temporal time series.” 2015. Web. 16 Apr 2021.

Vancouver:

Chang K. Topics in compositional, seasonal and spatial-temporal time series. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2021 Apr 16]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48411/.

Council of Science Editors:

Chang K. Topics in compositional, seasonal and spatial-temporal time series. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48411/

University of Oxford

11. Lim, Si Jie Bryan. Deep learning for time series prediction and decision making over time.

Degree: PhD, 2020, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:38843c7a-2123-4b5d-8454-2ae35a7ed6ce ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.823533

► In this thesis, we develop a collection of state-of-the-art deep learning models for time series forecasting. Primarily focusing on a closer alignment with traditional methods… (more)

▼ In this thesis, we develop a collection of state-of-the-art deep learning models for time series forecasting. Primarily focusing on a closer alignment with traditional methods in time series modelling, we adopt three main directions of research – 1) novel architectures, 2) hybrid models, and 3) feature extraction. Firstly, we propose two new architectures for general one-step-ahead and multi-horizon forecasting. With the Recurrent Neural Filter (RNF), we take a closer look at the relationship between recurrent neural networks and Bayesian filtering, so as to improve representation learning for one-step-ahead forecasts. For multi-horizon forecasting, we propose the Temporal Fusion Transformer (TFT) – an attention-based model designed to accommodate the full range of inputs present in common problem scenarios. Secondly, we investigate the use of hybrid models to enhance traditional quantitative models with deep learning components – using domain-specific knowledge can be used to guide neural network training. Through applications in finance (Deep Momentum Networks) and medicine (Disease-Atlas), we demonstrate that hybrid models can effectively improve forecasting performance over pure methods in either category. Finally, we explore the feature learning capabilities of deep neural networks to devise features for general forecasting models. Considering an application in systemic risk management, we devise the Autoencoder Reconstruction Ratio (ARR) – an indicator to measure the degree of co-movement between asset returns. When fed as an input into a variety of models, we show that the ARR can help to improve short-term predictions of various risk metrics. On top of improvements in forecasting performance, we also investigate extensions to enable decision support using deep neural networks, by helping users to better understand their data. With Recurrent Marginal Structural Networks (RMSNs), we introduce a general framework to train deep neural networks to learn causal effects over time, using ideas from marginal structural modelling in epidemiology. In addition, we also propose three practical interpretability use-cases for the TFT, demonstrating how attention weights can be analysed to provide insights into temporal dynamics.

Subjects/Keywords: time series analysis; deep learning

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APA (6^th Edition):

Lim, S. J. B. (2020). Deep learning for time series prediction and decision making over time. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:38843c7a-2123-4b5d-8454-2ae35a7ed6ce ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.823533

Chicago Manual of Style (16^th Edition):

Lim, Si Jie Bryan. “Deep learning for time series prediction and decision making over time.” 2020. Doctoral Dissertation, University of Oxford. Accessed April 16, 2021. http://ora.ox.ac.uk/objects/uuid:38843c7a-2123-4b5d-8454-2ae35a7ed6ce ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.823533.

MLA Handbook (7^th Edition):

Lim, Si Jie Bryan. “Deep learning for time series prediction and decision making over time.” 2020. Web. 16 Apr 2021.

Vancouver:

Lim SJB. Deep learning for time series prediction and decision making over time. [Internet] [Doctoral dissertation]. University of Oxford; 2020. [cited 2021 Apr 16]. Available from: http://ora.ox.ac.uk/objects/uuid:38843c7a-2123-4b5d-8454-2ae35a7ed6ce ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.823533.

Council of Science Editors:

Lim SJB. Deep learning for time series prediction and decision making over time. [Doctoral Dissertation]. University of Oxford; 2020. Available from: http://ora.ox.ac.uk/objects/uuid:38843c7a-2123-4b5d-8454-2ae35a7ed6ce ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.823533

Columbia University

12. Yousuf, Kashif. Essays in High Dimensional Time Series Analysis.

Degree: 2019, Columbia University

URL: https://doi.org/10.7916/d8-yfg6-4971

► Due to the rapid improvements in the information technology, high dimensional time series datasets are frequently encountered in a variety of fields such as macroeconomics,… (more)

▼ Due to the rapid improvements in the information technology, high dimensional time series datasets are frequently encountered in a variety of fields such as macroeconomics, finance, neuroscience, and meteorology. Some examples in economics and finance include forecasting low frequency macroeconomic indicators, such as GDP or inflation rate, or financial asset returns using a large number of macroeconomic and financial time series and their lags as possible covariates. In these settings, the number of candidate predictors (pT) can be much larger than the number of samples (T), and accurate estimation and prediction is made possible by relying on some form of dimension reduction. Given this ubiquity of time series data, it is surprising that few works on high dimensional statistics discuss the time series setting, and even fewer works have developed methods which utilize the unique features of time series data. This chapter consists of three chapters, and each one is self contained. The first chapter deals with high dimensional predictive regressions which are widely used in economics and finance. However, the theory and methodology is mainly developed assuming that the model is stationary with time invariant parameters. This is at odds with the prevalent evidence for parameter instability in economic time series. To remedy this, we present two L2 boosting algorithms for estimating high dimensional models in which the coefficients are modeled as functions evolving smoothly over time and the predictors are locally stationary. The first method uses componentwise local constant estimators as base learner, while the second relies on componentwise local linear estimators. We establish consistency of both methods, and address the practical issues of choosing the bandwidth for the base learners and the number of boosting iterations. In an extensive application to macroeconomic forecasting with many potential predictors, we find that the benefits to modeling time variation are substantial and are present across a wide range of economic series. Furthermore, these benefits increase with the forecast horizon and with the length of the time series available for estimation. This chapter is jointly written with Serena Ng. The second chapter deals with high dimensional non-linear time series models, and deals with the topic of variable screening/targeting predictors. Rather than assume a specific parametric model a priori, this chapter introduces several model free screening methods based on the partial distance correlation and developed specifically to deal with time dependent data. Methods are developed both for univariate models, such as nonlinear autoregressive models with exogenous predictors (NARX), and multivariate models such as linear or nonlinear VAR models. Sure screening properties are proved for our methods, which depend on the moment conditions, and the strength of dependence in the response and covariate processes, amongst other factors. Finite sample performance of our methods is shown through extensive…

Subjects/Keywords: Statistics; Economics; Time-series analysis; Time-series analysis – Data processing; Regression analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yousuf, K. (2019). Essays in High Dimensional Time Series Analysis. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/d8-yfg6-4971

Chicago Manual of Style (16^th Edition):

Yousuf, Kashif. “Essays in High Dimensional Time Series Analysis.” 2019. Doctoral Dissertation, Columbia University. Accessed April 16, 2021. https://doi.org/10.7916/d8-yfg6-4971.

MLA Handbook (7^th Edition):

Yousuf, Kashif. “Essays in High Dimensional Time Series Analysis.” 2019. Web. 16 Apr 2021.

Vancouver:

Yousuf K. Essays in High Dimensional Time Series Analysis. [Internet] [Doctoral dissertation]. Columbia University; 2019. [cited 2021 Apr 16]. Available from: https://doi.org/10.7916/d8-yfg6-4971.

Council of Science Editors:

Yousuf K. Essays in High Dimensional Time Series Analysis. [Doctoral Dissertation]. Columbia University; 2019. Available from: https://doi.org/10.7916/d8-yfg6-4971

13. Hamada, Ryuunosuke. Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/8739

Subjects/Keywords: time series analysis

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APA (6^th Edition):

Hamada, R. (n.d.). Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/8739

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hamada, Ryuunosuke. “Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed April 16, 2021. http://hdl.handle.net/10061/8739.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hamada, Ryuunosuke. “Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ.” Web. 16 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Hamada R. Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10061/8739.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Hamada R. Applying Nonparametric Bayesian Approach to Non-homogeneous Multiple Time Series towards Prediction of Driving Operations : 運転行動の予測に向けた不均質な複数時系列へのノンパラメトリックベイズ法の適用; ウンテンコウドウノヨソクニムケタフキンシツナフクスウジケイレツエノノンパラメトリックベイズホウノテキヨウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/8739

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Ryerson University

14. Ardalani-Farsa, Muhammad. Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments.

Degree: 2010, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A1347

► This dissertation aims to develop an effective and practical method to forecast chaotic time series. Chaotic behaviour has been observed in the areas of marketing,… (more)

Subjects/Keywords: Chaotic behavior in systems; Time-series analysis

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APA (6^th Edition):

Ardalani-Farsa, M. (2010). Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A1347

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ardalani-Farsa, Muhammad. “Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments.” 2010. Thesis, Ryerson University. Accessed April 16, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A1347.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ardalani-Farsa, Muhammad. “Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments.” 2010. Web. 16 Apr 2021.

Vancouver:

Ardalani-Farsa M. Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments. [Internet] [Thesis]. Ryerson University; 2010. [cited 2021 Apr 16]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1347.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ardalani-Farsa M. Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments. [Thesis]. Ryerson University; 2010. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1347

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Ryerson University

15. Ghoraani, Behnaz. Time-Frequency Feature Analysis.

Degree: 2010, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A1527

► Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very… (more)

▼ Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal.In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing. Advisors/Committee Members: Ryerson University (Degree grantor).

Subjects/Keywords: Signal processing – Mathematics; Time-series analysis

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APA (6^th Edition):

Ghoraani, B. (2010). Time-Frequency Feature Analysis. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A1527

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ghoraani, Behnaz. “Time-Frequency Feature Analysis.” 2010. Thesis, Ryerson University. Accessed April 16, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A1527.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ghoraani, Behnaz. “Time-Frequency Feature Analysis.” 2010. Web. 16 Apr 2021.

Vancouver:

Ghoraani B. Time-Frequency Feature Analysis. [Internet] [Thesis]. Ryerson University; 2010. [cited 2021 Apr 16]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1527.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghoraani B. Time-Frequency Feature Analysis. [Thesis]. Ryerson University; 2010. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1527

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

16. Robb, Loretta J. Estimation of the order of an autoregressive time series : a Bayesian approach.

Degree: PhD, Statistics, 1979, Oregon State University

URL: http://hdl.handle.net/1957/42411

► Finite order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the… (more)

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Robb, L. J. (1979). Estimation of the order of an autoregressive time series : a Bayesian approach. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/42411

Chicago Manual of Style (16^th Edition):

Robb, Loretta J. “Estimation of the order of an autoregressive time series : a Bayesian approach.” 1979. Doctoral Dissertation, Oregon State University. Accessed April 16, 2021. http://hdl.handle.net/1957/42411.

MLA Handbook (7^th Edition):

Robb, Loretta J. “Estimation of the order of an autoregressive time series : a Bayesian approach.” 1979. Web. 16 Apr 2021.

Vancouver:

Robb LJ. Estimation of the order of an autoregressive time series : a Bayesian approach. [Internet] [Doctoral dissertation]. Oregon State University; 1979. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1957/42411.

Council of Science Editors:

Robb LJ. Estimation of the order of an autoregressive time series : a Bayesian approach. [Doctoral Dissertation]. Oregon State University; 1979. Available from: http://hdl.handle.net/1957/42411

Oregon State University

17. Tang, Zhigiang. Bilinear stochastic processes and time series.

Degree: PhD, Electrical and Computer Engineering, 1987, Oregon State University

URL: http://hdl.handle.net/1957/39933

► In engineering, biology, ecology, medicine, economics and social science, some processes are essentially bilinear, and some could be approximated accurately by bilinear processes under certain… (more)

▼ In engineering, biology, ecology, medicine, economics and social science, some processes are essentially bilinear, and some could be approximated accurately by bilinear processes under certain conditions. In this thesis the bilinear stochastic process and bilinear time series are discussed. Bilinear models essentially are nonlinear; the superposition rule is not valid. A useful property, which characterizes the bilinear feature among the nonlinear ones, is emphasized. The solutions of deterministic bilinear systems and bilinear stochastic processes are given. The direct method uses the Lie algebraic structure. For bilinear stochastic processes, the decomposition to a cascade form is a generalization of the Volterra-series expansion. Because a correction term exists in bilinear stochastic differential equations, the decomposition has two different forms; both of them are convergent. The lth -order stationarity and asymptotic stationarity of bilinear stochastic processes and time series are well defined, and the conditions on parameters for lth -order stationarity are derived. Affine bilinear models in time-series form are shown to be more general than bilinear models, and more readily fit certain data. A special high-order scalar affine bilinear time-series model can be transferred to a first-order, vector, affine, bilinear model, but need higher dimension than the linear ARMA model. For first-order affine bilinear time series two possible methods of parameter estimation are presented. The moment method uses the relationships between the parameters, and the second and third-moments to estimate parameters. The inverse method uses the output data to estimate the input, which is compared with the standard white Gaussian random sequence, and the method chooses the parameters of the model to make certain criterion optimal. For the general non-Gaussian time series an identification procedure using the inverse method is proposed. Some examples of analysis and parameter estimation of bilinear models are provided. Advisors/Committee Members: Mohler, Ronald R. (advisor).

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Tang, Z. (1987). Bilinear stochastic processes and time series. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/39933

Chicago Manual of Style (16^th Edition):

Tang, Zhigiang. “Bilinear stochastic processes and time series.” 1987. Doctoral Dissertation, Oregon State University. Accessed April 16, 2021. http://hdl.handle.net/1957/39933.

MLA Handbook (7^th Edition):

Tang, Zhigiang. “Bilinear stochastic processes and time series.” 1987. Web. 16 Apr 2021.

Vancouver:

Tang Z. Bilinear stochastic processes and time series. [Internet] [Doctoral dissertation]. Oregon State University; 1987. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1957/39933.

Council of Science Editors:

Tang Z. Bilinear stochastic processes and time series. [Doctoral Dissertation]. Oregon State University; 1987. Available from: http://hdl.handle.net/1957/39933

Oregon State University

18. Chen, Kuei-Lin. Performance evaluation and design of multiple time series based forecasting systems.

Degree: PhD, Industrial and General Engineering, 1976, Oregon State University

URL: http://hdl.handle.net/1957/43755

► The study evaluates performances of three multiple time series (MTS) forecasting methods; investigates possible improvements in MTS forecasting operations, and proposes a multiple time series… (more)

▼ The study evaluates performances of three multiple time series (MTS) forecasting methods; investigates possible improvements in MTS forecasting operations, and proposes a multiple time series based forecasting system. Time series considered are finite, linear, covariance stationary, and discrete. Specific objectives for the thesis were: (a) conducting comparative studies of MTS estimation methods; (b) investigating the effectiveness of composite forecasts in MTS analysis; (c) examining performance of process control procedures in MTS forecasting operations; and (d) proposing an overall approach to MTS estimation, model building and forecasting procedures. Multivariate exponential smoothing, multivariate Yule-Walker equations, and Chitturi's discounted least squares parameter estimation methods were used in the study. Time series schemes which are most suitable for using these estimation methods are multiple autoregressive processes, MAR(s,p); and mixed autoregressivemoving average processes, MARMA(s, 1, 1). Comparative studies were conducted on selected real economic time series. Outcomes of forecasting were measured in terms of weighted mean square errors. Computational efforts were recorded and compared. Proper data treatments were taken to ensure desired characteristics such as stationarity, and nonseasonality. The techniques of composite forecasting were studied and applied to MTS analysis. Performance of all four alternatives for composite forecasts were reviewed and favorable results were reported. In an effort to reduce computation expenses in MTS analysis, process control procedures were implemented. A trivariate MTS model was then used to demonstrate the application of process control procedures in MTS forecasting operations. An overall approach to MTS estimation, model building and forecasting procedure has been developed. It is a two-phase procedure. The estimation phase is a multi-variable analogue of the Box-Jenkins iterative process for model building in single time series analysis. Process control procedures and composite forecasting techniques are incorporated in the second phase. The complete process of the proposed multiple time series based forecasting systems is illustrated through a trivariate MTS model. Advisors/Committee Members: Love, Stephen F. (advisor), Love, Stephen F. (committee member).

Subjects/Keywords: Time-series analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, K. (1976). Performance evaluation and design of multiple time series based forecasting systems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/43755

Chicago Manual of Style (16^th Edition):

Chen, Kuei-Lin. “Performance evaluation and design of multiple time series based forecasting systems.” 1976. Doctoral Dissertation, Oregon State University. Accessed April 16, 2021. http://hdl.handle.net/1957/43755.

MLA Handbook (7^th Edition):

Chen, Kuei-Lin. “Performance evaluation and design of multiple time series based forecasting systems.” 1976. Web. 16 Apr 2021.

Vancouver:

Chen K. Performance evaluation and design of multiple time series based forecasting systems. [Internet] [Doctoral dissertation]. Oregon State University; 1976. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1957/43755.

Council of Science Editors:

Chen K. Performance evaluation and design of multiple time series based forecasting systems. [Doctoral Dissertation]. Oregon State University; 1976. Available from: http://hdl.handle.net/1957/43755

Penn State University

19. Chattopadhyay, Pritthi. Data-Driven Modeling and Pattern Recognition of Dynamical Systems.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15396pxc271

► Human-engineered complex systems need to be monitored consistently to ensure their safety and efficiency, which might be affected due to degradation over time or unanticipated… (more)

▼ Human-engineered complex systems need to be monitored consistently to ensure their safety and efficiency, which might be affected due to degradation over time or unanticipated disturbances. For systems that change at a fast time scale, instead of active health monitoring, preventative system design is more feasible and effective. Both active health monitoring and preventative system design can be done using physics-based or data-driven models. In comparison to physics-based models, data-driven models do not require knowledge of the underlying system dynamics; they determine the relation between the relevant input and output variables from a training data set. This is useful when there is lack of understanding of the system dynamics or the developed models are inadequate. One such scenario is combustion, where the difficulties include nonlinear dynamics involving several input parameters; existence of bifurcations in the dynamic behavior and extremely high sensitivity of the combustor behavior to even small changes in some of the design parameters. Similarly, for batteries, sufficient knowledge of the electrochemical characteristics is necessary to develop models for parameter �identification at different operating points of the nonlinear battery dynamics. This dissertation develops dynamic data-driven models for combustor design and battery health monitoring, using concepts of machine learning and statistics, which do not require much knowledge of the underlying system dynamics. But the performance of a data-driven algorithm depends on many factors namely: 1. Availability of training data which covers all events of interest. For applications involving time series data, each individual time series must also be sufficiently long, to encompass the dynamics of the underlying system for each event. 2. The quality of extracted features, i.e. whether they capture all the information about the system. 3. The relation between the relevant input and output variables remaining constant during the time the algorithm is being trained. Hence, the second part of the dissertation develops an unsupervised algorithm for scenarios where condition (iii) might not hold; quanti�es the e�ect of the nonconformity of condition (i) on the performance of an algorithm and proposes a feature extraction algorithm to ensure conformity of condition (ii). Advisors/Committee Members: Asok Ray, Dissertation Advisor/Co-Advisor, Asok Ray, Committee Chair/Co-Chair, Christopher Rahn, Committee Member, Minghui Zhu, Committee Member, Shashi Phoha, Outside Member.

Subjects/Keywords: data-driven modeling; time series analysis

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APA (6^th Edition):

Chattopadhyay, P. (2018). Data-Driven Modeling and Pattern Recognition of Dynamical Systems. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15396pxc271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chattopadhyay, Pritthi. “Data-Driven Modeling and Pattern Recognition of Dynamical Systems.” 2018. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/15396pxc271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chattopadhyay, Pritthi. “Data-Driven Modeling and Pattern Recognition of Dynamical Systems.” 2018. Web. 16 Apr 2021.

Vancouver:

Chattopadhyay P. Data-Driven Modeling and Pattern Recognition of Dynamical Systems. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/15396pxc271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chattopadhyay P. Data-Driven Modeling and Pattern Recognition of Dynamical Systems. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15396pxc271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

20. Perrie, Jonathan. Modelling Chart Trajectories using Song Features.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/14937

► Over the years, hit song science has been a controversial topic within music information retrieval. Researchers have debated whether an unbiased dataset can be constructed… (more)

Subjects/Keywords: Popular music; Time-series analysis; Machine learning

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APA (6^th Edition):

Perrie, J. (2019). Modelling Chart Trajectories using Song Features. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14937

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Perrie, Jonathan. “Modelling Chart Trajectories using Song Features.” 2019. Thesis, University of Waterloo. Accessed April 16, 2021. http://hdl.handle.net/10012/14937.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Perrie, Jonathan. “Modelling Chart Trajectories using Song Features.” 2019. Web. 16 Apr 2021.

Vancouver:

Perrie J. Modelling Chart Trajectories using Song Features. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10012/14937.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Perrie J. Modelling Chart Trajectories using Song Features. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14937

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Nelson Mandela Metropolitan University

21. Van Niekerk, Bracken. Application of hidden Markov models and their extensions to animal movement data.

Degree: 2018, Nelson Mandela Metropolitan University

URL: http://hdl.handle.net/10948/23835

► Hidden Markov Models (HMMs) have become increasingly popular in animal movement studies as they provide a flexible modelling approach and take the correlation between successive… (more)

▼ Hidden Markov Models (HMMs) have become increasingly popular in animal movement studies as they provide a flexible modelling approach and take the correlation between successive observations into account. They can segment the movement paths into latent states, which can be considered as rough proxies for the behaviours of the animals. This study comprises of two sections, both involving the application of HMMs to large terrestrial mammal movement data. Usually step lengths representing the displacement distances between successive observations, turning angles measuring the tortuosity, or a bivariate input of both variables are used as inputs in the models. It has been found in the literature that the turning angle is either included in the modelling process or it is excluded without much justification for doing so. The first part of this study investigates the nfluence of the turning angle on the model output and resultant interpretations of the HMMs when modelling the trajectories of large terrestrial mammals in southern Africa. Results revealed at different time scales, and for both predator and herbivore species in this study, that the turning angle does not influence the state allocation of the HMMs, which is the main output in terms of interpreting the behaviours of the animals. It is thought in most cases that the inclusion of the turning angle overcomplicates the models unnecessarily without contributing any additional information in terms of the behavioural interpretations or improving the overall fit of the models. This was found for the variety of movements of the species under observation in this study. The second part of this study attempts to validate the state allocation of the HMMs fitted to eland trajectories in the Greater Addo Elephant National Park in the Eastern Cape, with the use of camera trap data. This presented a unique opportunity as this type of data is mainly used for abundance or capture-recapture studies, and the HMMs are rarely validated as the true behaviours of the animals are seldom known. Results revealed that the same diel patterns were detected by the HMMs that were shown by the classified camera trap data. Direct comparisons of the observations where the dates and times matched for the telemetry and camera trap data could be done in several rare instances, which revealed many similarities. Although it was not an ideal comparison, the camera trap data provided a rough validation of the state allocation of the HMMs used in the study.

Subjects/Keywords: Markov processes; Animal locomotion; Time-series analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Van Niekerk, B. (2018). Application of hidden Markov models and their extensions to animal movement data. (Thesis). Nelson Mandela Metropolitan University. Retrieved from http://hdl.handle.net/10948/23835

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Van Niekerk, Bracken. “Application of hidden Markov models and their extensions to animal movement data.” 2018. Thesis, Nelson Mandela Metropolitan University. Accessed April 16, 2021. http://hdl.handle.net/10948/23835.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Van Niekerk, Bracken. “Application of hidden Markov models and their extensions to animal movement data.” 2018. Web. 16 Apr 2021.

Vancouver:

Van Niekerk B. Application of hidden Markov models and their extensions to animal movement data. [Internet] [Thesis]. Nelson Mandela Metropolitan University; 2018. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10948/23835.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Van Niekerk B. Application of hidden Markov models and their extensions to animal movement data. [Thesis]. Nelson Mandela Metropolitan University; 2018. Available from: http://hdl.handle.net/10948/23835

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of KwaZulu-Natal

22. Dralle, Bruce. Modelling volatility in financial time series.

Degree: MS, Statistics and actuarial science, 2011, University of KwaZulu-Natal

URL: http://hdl.handle.net/10413/8504

► The objective of this dissertation is to model the volatility of financial time series data using ARCH, GARCH and stochastic volatility models. It is found… (more)

Subjects/Keywords: Statistics and actuarial science.; Time-series analysis.

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APA (6^th Edition):

Dralle, B. (2011). Modelling volatility in financial time series. (Masters Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/8504

Chicago Manual of Style (16^th Edition):

Dralle, Bruce. “Modelling volatility in financial time series.” 2011. Masters Thesis, University of KwaZulu-Natal. Accessed April 16, 2021. http://hdl.handle.net/10413/8504.

MLA Handbook (7^th Edition):

Dralle, Bruce. “Modelling volatility in financial time series.” 2011. Web. 16 Apr 2021.

Vancouver:

Dralle B. Modelling volatility in financial time series. [Internet] [Masters thesis]. University of KwaZulu-Natal; 2011. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10413/8504.

Council of Science Editors:

Dralle B. Modelling volatility in financial time series. [Masters Thesis]. University of KwaZulu-Natal; 2011. Available from: http://hdl.handle.net/10413/8504

Delft University of Technology

23. Verhoeven, Vincent (author). Satellite-Based Analysis of Vegetation Trends in Europe.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:952a33b0-e5dd-4228-a2a1-443210cf4f37

► The aim of this thesis project is to investigate the temporal changes of vegetation in Europe using statistical analysis and machine learning. A methodology is… (more)

Subjects/Keywords: NDVI; Vegetation; Satellite; Time Series Analysis; Classification

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APA (6^th Edition):

Verhoeven, V. (. (2020). Satellite-Based Analysis of Vegetation Trends in Europe. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:952a33b0-e5dd-4228-a2a1-443210cf4f37

Chicago Manual of Style (16^th Edition):

Verhoeven, Vincent (author). “Satellite-Based Analysis of Vegetation Trends in Europe.” 2020. Masters Thesis, Delft University of Technology. Accessed April 16, 2021. http://resolver.tudelft.nl/uuid:952a33b0-e5dd-4228-a2a1-443210cf4f37.

MLA Handbook (7^th Edition):

Verhoeven, Vincent (author). “Satellite-Based Analysis of Vegetation Trends in Europe.” 2020. Web. 16 Apr 2021.

Vancouver:

Verhoeven V(. Satellite-Based Analysis of Vegetation Trends in Europe. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 16]. Available from: http://resolver.tudelft.nl/uuid:952a33b0-e5dd-4228-a2a1-443210cf4f37.

Council of Science Editors:

Verhoeven V(. Satellite-Based Analysis of Vegetation Trends in Europe. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:952a33b0-e5dd-4228-a2a1-443210cf4f37

Baylor University

24. Agrawal, Mohit, 1985-. Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift.

Degree: PhD, Baylor University. Dept. of Geosciences., 2016, Baylor University

URL: http://hdl.handle.net/2104/9653

► We develop and apply new modeling methods that make use of disparate but complementary seismic “functionals,” such as receiver functions and dispersion curves, and model… (more)

▼ We develop and apply new modeling methods that make use of disparate but complementary seismic “functionals,” such as receiver functions and dispersion curves, and model them using a global optimization method called “Very Fast Simulated Annealing” (VFSA). We apply aspects of the strategy, which we call “Multi Objective Optimization for Seismology” (MOOS), to three broadband seismic datasets: a sparse network in the Middle East, a closely-spaced linear transect across Texas Gulf Coastal Plain, and a 2D array in SE New Mexico and West Texas (the eastern flank of the Rio Grande Rift). First, seismic velocity models are found, along with quantitative uncertainty estimates, for eleven sites in the Middle East by jointly modeling Ps and Sp receiver functions and surface (Rayleigh) wave group velocity dispersion curves. These tools demonstrate cases in which joint modeling of disparate and complementary functionals provide better constraints on model parameters than a single functional alone. Next, we generate a 2D stacked receiver function image with a common conversion point stacking technique using seismic data from a linear array of 22 broadband stations deployed across Texas’s Gulf Coastal Plain. The image is migrated using velocity models found by modeling dispersion curves computed via ambient noise cross-correlation. Our results show that the Moho disappears outboard of the Balcones Fault Zone and that a significant, negative-polarity discontinuity exists beneath the Coastal Plain. Lastly, we stack and depth-migrate Ps and Sp receiver functions computed from data recorded by broadband stations deployed by the SIEDCAR (Seismic Investigation of Edge Driven Convection Association with Rio Grande Rift) project. To find P-and Swave velocity models for receiver function migration, we develop and apply a technique that is analogous to “velocity analysis” in seismic reflection processing. The resulting 3D image reveals gaps in the seismically-determined lithosphere-asthenosphere boundary (LAB) and Moho beneath dramatically uplifted topography and above a distinct fast anomaly (found independently via seismic travel time tomography). We speculate that this gap is the result of large-scale lithospheric removal associated with east-west extension and the northward propagation of the Rio Grande Rift. Advisors/Committee Members: Pulliam, Robert Jay. (advisor).

Subjects/Keywords: Time-series analysis. Inverse theory. Geophysics.

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APA (6^th Edition):

Agrawal, Mohit, 1. (2016). Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift. (Doctoral Dissertation). Baylor University. Retrieved from http://hdl.handle.net/2104/9653

Chicago Manual of Style (16^th Edition):

Agrawal, Mohit, 1985-. “Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift.” 2016. Doctoral Dissertation, Baylor University. Accessed April 16, 2021. http://hdl.handle.net/2104/9653.

MLA Handbook (7^th Edition):

Agrawal, Mohit, 1985-. “Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift.” 2016. Web. 16 Apr 2021.

Vancouver:

Agrawal, Mohit 1. Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift. [Internet] [Doctoral dissertation]. Baylor University; 2016. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2104/9653.

Council of Science Editors:

Agrawal, Mohit 1. Multi Objective Optimization for Seismology (MOOS), with application to the Middle East, the Texas Gulf Coast, and the Rio Grande Rift. [Doctoral Dissertation]. Baylor University; 2016. Available from: http://hdl.handle.net/2104/9653

Columbia University

25. Paparrizos, Ioannis. Fast, Scalable, and Accurate Algorithms for Time-Series Analysis.

Degree: 2018, Columbia University

URL: https://doi.org/10.7916/D80K3S4B

► Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The… (more)

Subjects/Keywords: Computer science; Algorithms; Time-series analysis

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APA (6^th Edition):

Paparrizos, I. (2018). Fast, Scalable, and Accurate Algorithms for Time-Series Analysis. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D80K3S4B

Chicago Manual of Style (16^th Edition):

Paparrizos, Ioannis. “Fast, Scalable, and Accurate Algorithms for Time-Series Analysis.” 2018. Doctoral Dissertation, Columbia University. Accessed April 16, 2021. https://doi.org/10.7916/D80K3S4B.

MLA Handbook (7^th Edition):

Paparrizos, Ioannis. “Fast, Scalable, and Accurate Algorithms for Time-Series Analysis.” 2018. Web. 16 Apr 2021.

Vancouver:

Paparrizos I. Fast, Scalable, and Accurate Algorithms for Time-Series Analysis. [Internet] [Doctoral dissertation]. Columbia University; 2018. [cited 2021 Apr 16]. Available from: https://doi.org/10.7916/D80K3S4B.

Council of Science Editors:

Paparrizos I. Fast, Scalable, and Accurate Algorithms for Time-Series Analysis. [Doctoral Dissertation]. Columbia University; 2018. Available from: https://doi.org/10.7916/D80K3S4B

Columbia University

26. Zhang, Jing. Time Series Modeling with Shape Constraints.

Degree: 2017, Columbia University

URL: https://doi.org/10.7916/D84X5M55

► This thesis focuses on the development of semiparametric estimation methods for a class of time series models using shape constraints. Many of the existing time… (more)

▼ This thesis focuses on the development of semiparametric estimation methods for a class of time series models using shape constraints. Many of the existing time series models assume the noise follows some known parametric distributions. Typical examples are the Gaussian and t distributions. Then the model parameters are estimated by maximizing the resultant likelihood function. As an example, the autoregressive moving average (ARMA) models (Brockwell and Davis, 2009) assume Gaussian noise sequence and are estimated under the causal-invertible constraint by maximizing the Gaussian likelihood. Although the same estimates can also be used in the causal-invertible non-Gaussian case, they are not asymptotically optimal (Rosenblatt, 2012). Moreover, for the noncausal/noninvertible cases, the Gaussian likelihood estimation procedure is not applicable, since any second-order based methods cannot distinguish between causal-invertible and noncausal/noninvertible models (Brockwell and Davis,2009). As a result, many estimation methods for noncausal/noninvertible ARMA models assume the noise follows a known non-Gaussian distribution, like a Laplace distribution or a t distribution. To relax this distributional assumption and allow noncausal/noninvertible models, we borrow ideas from nonparametric shape-constraint density estimation and propose a semiparametric estimation procedure for general ARMA models by projecting the underlying noise distribution onto the space of log-concave measures (Cule and Samworth, 2010; Dümbgen et al., 2011). We show the maximum likelihood estimators in this semiparametric setting are consistent. In fact, the MLE is robust to the misspecification of log-concavity in cases where the true distribution of the noise is close to its log-concave projection. We derive a lower bound for the best asymptotic variance of regular estimators at rate sqrt(n) for AR models and construct a semiparametric efficient estimator. We also consider modeling time series of counts with shape constraints. Many of the formulated models for count time series are expressed via a pair of generalized state-space equations. In this set-up, the observation equation specifies the conditional distribution of the observation Yt at time t given a state-variable Xt. For count time series, this conditional distribution is usually specified as coming from a known parametric family such as the Poisson or the Negative Binomial distribution. To relax this formal parametric framework, we introduce a concave shape constraint into the one-parameter exponential family. This essentially amounts to assuming that the reference measure is log-concave. In this fashion, we are able to extend the class of observation-driven models studied in Davis and Liu (2016). Under this formulation, there exists a stationary and ergodic solution to the state-space model. In this new modeling framework, we consider the inference problem of estimating both the parameters of the mean model and the log-concave function, corresponding to the reference measure.…

Subjects/Keywords: Statistics; Time-series analysis – Mathematical models

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APA (6^th Edition):

Zhang, J. (2017). Time Series Modeling with Shape Constraints. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D84X5M55

Chicago Manual of Style (16^th Edition):

Zhang, Jing. “Time Series Modeling with Shape Constraints.” 2017. Doctoral Dissertation, Columbia University. Accessed April 16, 2021. https://doi.org/10.7916/D84X5M55.

MLA Handbook (7^th Edition):

Zhang, Jing. “Time Series Modeling with Shape Constraints.” 2017. Web. 16 Apr 2021.

Vancouver:

Zhang J. Time Series Modeling with Shape Constraints. [Internet] [Doctoral dissertation]. Columbia University; 2017. [cited 2021 Apr 16]. Available from: https://doi.org/10.7916/D84X5M55.

Council of Science Editors:

Zhang J. Time Series Modeling with Shape Constraints. [Doctoral Dissertation]. Columbia University; 2017. Available from: https://doi.org/10.7916/D84X5M55

Michigan State University

27. Shin, Yongcheol. Testing economic time series for stationarity and nonstationarity.

Degree: PhD, Department of Economics, 1992, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:24661

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Shin, Y. (1992). Testing economic time series for stationarity and nonstationarity. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:24661

Chicago Manual of Style (16^th Edition):

Shin, Yongcheol. “Testing economic time series for stationarity and nonstationarity.” 1992. Doctoral Dissertation, Michigan State University. Accessed April 16, 2021. http://etd.lib.msu.edu/islandora/object/etd:24661.

MLA Handbook (7^th Edition):

Shin, Yongcheol. “Testing economic time series for stationarity and nonstationarity.” 1992. Web. 16 Apr 2021.

Vancouver:

Shin Y. Testing economic time series for stationarity and nonstationarity. [Internet] [Doctoral dissertation]. Michigan State University; 1992. [cited 2021 Apr 16]. Available from: http://etd.lib.msu.edu/islandora/object/etd:24661.

Council of Science Editors:

Shin Y. Testing economic time series for stationarity and nonstationarity. [Doctoral Dissertation]. Michigan State University; 1992. Available from: http://etd.lib.msu.edu/islandora/object/etd:24661

Michigan State University

28. Tieslau, Margie A. Strongly dependent economic time series : theory and applications.

Degree: PhD, Department of Economics, 1992, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:24678

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Tieslau, M. A. (1992). Strongly dependent economic time series : theory and applications. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:24678

Chicago Manual of Style (16^th Edition):

Tieslau, Margie A. “Strongly dependent economic time series : theory and applications.” 1992. Doctoral Dissertation, Michigan State University. Accessed April 16, 2021. http://etd.lib.msu.edu/islandora/object/etd:24678.

MLA Handbook (7^th Edition):

Tieslau, Margie A. “Strongly dependent economic time series : theory and applications.” 1992. Web. 16 Apr 2021.

Vancouver:

Tieslau MA. Strongly dependent economic time series : theory and applications. [Internet] [Doctoral dissertation]. Michigan State University; 1992. [cited 2021 Apr 16]. Available from: http://etd.lib.msu.edu/islandora/object/etd:24678.

Council of Science Editors:

Tieslau MA. Strongly dependent economic time series : theory and applications. [Doctoral Dissertation]. Michigan State University; 1992. Available from: http://etd.lib.msu.edu/islandora/object/etd:24678

Michigan State University

29. Hwang, Jaeyoun. GLS detrending and the power of unit root and stationarity tests.

Degree: PhD, Department of Economics, 1993, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:23087

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Hwang, J. (1993). GLS detrending and the power of unit root and stationarity tests. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:23087

Chicago Manual of Style (16^th Edition):

Hwang, Jaeyoun. “GLS detrending and the power of unit root and stationarity tests.” 1993. Doctoral Dissertation, Michigan State University. Accessed April 16, 2021. http://etd.lib.msu.edu/islandora/object/etd:23087.

MLA Handbook (7^th Edition):

Hwang, Jaeyoun. “GLS detrending and the power of unit root and stationarity tests.” 1993. Web. 16 Apr 2021.

Vancouver:

Hwang J. GLS detrending and the power of unit root and stationarity tests. [Internet] [Doctoral dissertation]. Michigan State University; 1993. [cited 2021 Apr 16]. Available from: http://etd.lib.msu.edu/islandora/object/etd:23087.

Council of Science Editors:

Hwang J. GLS detrending and the power of unit root and stationarity tests. [Doctoral Dissertation]. Michigan State University; 1993. Available from: http://etd.lib.msu.edu/islandora/object/etd:23087

Michigan State University

30. Lee, Dongin. Asymtotic theory for long-memory time series.

Degree: PhD, Department of Economics, 1994, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:24512

Subjects/Keywords: Time-series analysis

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APA (6^th Edition):

Lee, D. (1994). Asymtotic theory for long-memory time series. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:24512

Chicago Manual of Style (16^th Edition):

Lee, Dongin. “Asymtotic theory for long-memory time series.” 1994. Doctoral Dissertation, Michigan State University. Accessed April 16, 2021. http://etd.lib.msu.edu/islandora/object/etd:24512.

MLA Handbook (7^th Edition):

Lee, Dongin. “Asymtotic theory for long-memory time series.” 1994. Web. 16 Apr 2021.

Vancouver:

Lee D. Asymtotic theory for long-memory time series. [Internet] [Doctoral dissertation]. Michigan State University; 1994. [cited 2021 Apr 16]. Available from: http://etd.lib.msu.edu/islandora/object/etd:24512.

Council of Science Editors:

Lee D. Asymtotic theory for long-memory time series. [Doctoral Dissertation]. Michigan State University; 1994. Available from: http://etd.lib.msu.edu/islandora/object/etd:24512

Open Access Theses and Dissertations