Multiscale Forecasting Models by Lida Mercedes Barba Maggi
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Multiscale Forecasting Models

Multiscale Forecasting Models


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About the Book

Preface

1. Time Series and Forecasting

1.1. Introduction

1.2. Time series

1.3. Linear Autoregressive Models

1.4. Artificial Neural Networks

1.5. Hybrid models

1.5.1. Singular Spectrum Analysis

1.5.2. Wavelet Transform

1.6. Forecasting Accuracy Measures

1.7. Empirical Applications

1.7.1. Traffic Accidents Forecasting based on AR, ANNs and Hybrid models.

1.7.2. Anchovy Stock Forecasting based on AR, ANNs and Hybrid models.

1.7.3. Sardine Stock Forecasting based on AR, ANNs and Hybrid models.

2. Decomposition methods based on Singular Value Decomposition of a Hankel matrix

2.1. Introduction

2.2. Eigenvalues and Eigenvectors

2.3. Theorem of Singular Values Decomposition

2.4. One-level Singular Value Decomposition of a Hankel matrix

2.4.1. Embedding

2.4.2. Decomposition

2.4.3. Unembedding

2.4.4. Window Length Selection

2.5. Multi-level Singular Value Decomposition of a Hankel matrix

2.5.1. Embedding

2.5.2. Decomposition

2.5.3. Unembedding

2.5.4. Singular Spectrum Rate

2.6. Empirical Applications

2.6.1. Extraction of Components from traffic accidents time series based on HSVD and MSVD

2.6.2. Extraction of Components from fishery time series based on HSVD and MSVD

3. Forecasting based on components

3.1. Introduction

3.2. One-step ahead forecasting

3.3. Multi-step ahead forecasting

3.3.1. Direct Strategy

3.3.2. MIMO Strategy

3.4. Empirical Applications

3.4.1. Forecasting of traffic accidents based on HSVD and MSVD

3.4.2. Forecasting of anchovy stock based on HSVD and MSVD

3.4.3. Forecasting of sardine stock based on HSVD and MSVD

List of Figures

List of Tables

List of Acronyms

List of Symbols

References


About the Author:

Lida Mercedes Barba Maggi earned a PhD degree in Informatics Engineering from the Pontificia Universidad Católica de Valparaíso, 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.


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Product Details
  • ISBN-13: 9783030069506
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 124
  • Spine Width: 8 mm
  • Width: 156 mm
  • ISBN-10: 3030069508
  • Publisher Date: 03 Jan 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Weight: 272 gr


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