Overview
- The book is unique because it contains two new and competitive methods for time series decomposition to improve the accuracy of auto-regressive models
- The methods are presented in detail through relevant applications
- Additionally, the methods are compared with other techniques which are conventionally used in forecasting
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Table of contents (4 chapters)
Keywords
About this book
This book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models.
Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters.
The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs.
Authors and Affiliations
About the author
Lida Mercedes Barba Maggi earned a PhD degree in Informatics Engineering from the Pontificia Universidad Católica de ValparaÃso, Chile, in 2017. She is currently affiliated with the Universidad Nacional de Chimborazo in Ecuador. Her research interests include Analysis of time series, Forecast and estimate based on mathematical and statistical models, Forecast and estimate based on artificial intelligence, and Optimization Algorithms.
Bibliographic Information
Book Title: Multiscale Forecasting Models
Authors: Lida Mercedes Barba Maggi
DOI: https://doi.org/10.1007/978-3-319-94992-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-94991-8Published: 31 August 2018
Softcover ISBN: 978-3-030-06950-6Published: 03 January 2019
eBook ISBN: 978-3-319-94992-5Published: 23 August 2018
Edition Number: 1
Number of Pages: XXIV, 124
Number of Illustrations: 2 b/w illustrations, 89 illustrations in colour
Topics: Artificial Intelligence, Probability and Statistics in Computer Science, Algebra