目次

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