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Linear Time Series with MATLAB and OCTAVE (Paperback, 1st ed. 2019)
Loot Price: R3,024
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Linear Time Series with MATLAB and OCTAVE (Paperback, 1st ed. 2019)
Series: Statistics and Computing
Expected to ship within 10 - 15 working days
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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.
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