Automatic Time Series Forecasting Using Neural Networks State Space And Arimax Models Examples With R
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AUTOMATIC TIME SERIES FORECASTING Using NEURAL NETWORKS, STATE SPACE and ARIMAX MODELS. Examples with R
This book delves into the prediction with time series by automatic methods. In the first place, neural network models are taken into account, which allow obtaining very precise results for predictions. Second, we work with state space models, which are also suitable for automatic prediction. Third, automatic predictions are taken into account in the case of models with intervention analysis. Additionally, the ARIMAX models or models of the transfer function that extend the ARIMA models are considered in the case of using external variables that guide the predictions of the objective variable. Finally, automatic and non-automatic forecasts are compared, including the more general seasonal case.
UNIVARIATE TIME SERIES FORECASTING. BOX JENKINS METHODOLOGY: ARIMA MODELS. Examples with R
This book develops the Box and Jenkins methodology for the prediction of time series through the ARIMA models. The book begins by introducing the concepts needed to make univariate time series predictions. Next, the identification, estimation and prediction of the ARIMA models is deepened, both in the non-seasonal field and in the seasonal field. An important part of the content is the automatic prediction methods, including the use of neural networks and the space of the states to obtain improved predictions of time series. The intervention models that collect the effects of atypicalities in obtaining predictions are discussed below. Finally, the transfer function models or ARIMAX models that use external continuous regressors to guide the predictions of a time series are considered. A great variety of examples and exercises solved with R. are presented.
State Space Modeling of Time Series
Author: Masanao Aoki
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-09
In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.