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Times Series Analysis

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Multiscale Forecasting Models

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Abstract

Discrete time series are valuable sources of information for supporting planning activities and designing strategies for decision-making. Multiple demand coming from diverse sectors such as, transport, fishery, economy, and finances are concerned into obtaining information in advance to improve their productivity and efficiency. Often is observed the presence of high variability in a time series, this situation obey to trend, seasonal variation, cyclic fluctuation, or irregular fluctuation. Classical methods are presented to analyze a time series coming from relevant domains. Besides some accuracy measures are presented, those based on absolute errors, squared errors, and some efficiency criteria which make emphasis on sensitivity to significant overfitting or underfitting when the observed values are very large or very small.

Finally in this chapter is presented an empirical application of autoregressive linear and autoregressive nonlinear models to forecast the number of injured people in traffic accidents in Valparaíso. The models are designed and implemented for one step ahead forecasting, their performance is evaluated through different accuracy metrics.

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Authors and Affiliations

  1. Universidad Nacional de Chimborazo, Riobamba, Ecuador

    Lida Mercedes Barba Maggi

  2. Pontifícia Universidad Católica de Valparaíso, Valparaíso, Chile

    Lida Mercedes Barba Maggi

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  1. Lida Mercedes Barba Maggi
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Barba Maggi, L.M. (2018). Times Series Analysis. In: Multiscale Forecasting Models. Springer, Cham. https://doi.org/10.1007/978-3-319-94992-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-94992-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94991-8

  • Online ISBN: 978-3-319-94992-5

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