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Linear Time Series with MATLAB and OCTAVE

  • Víctor Gómez

Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Víctor Gómez
    Pages 1-20
  3. Víctor Gómez
    Pages 21-120
  4. Víctor Gómez
    Pages 121-172
  5. Víctor Gómez
    Pages 173-223
  6. Víctor Gómez
    Pages 225-236
  7. Víctor Gómez
    Pages 237-244
  8. Víctor Gómez
    Pages 245-262
  9. Víctor Gómez
    Pages 263-278
  10. Víctor Gómez
    Pages 281-304
  11. Víctor Gómez
    Pages 305-332
  12. Back Matter
    Pages 333-339

About this book

Introduction

This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform.

The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc.

This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details. 

Keywords

Linear time series MATLAB State space models Kalman filter Univariate time series Multivariate time series Signal extraction OCTAVE platform Package SSMMATLAB Model estimation VARMA and ARIMA models VARMAX and transfer function models

Authors and affiliations

  • Víctor Gómez
    • 1
  1. 1.General Directorate of BudgetsMinistry of Finance and Public AdministrationsMadridSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-20790-8
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-20789-2
  • Online ISBN 978-3-030-20790-8
  • Series Print ISSN 1431-8784
  • Series Online ISSN 2197-1706
  • Buy this book on publisher's site
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