Time Series Analysis and Modeling to Forecast: a Survey

TL;DR

This comprehensive survey reviews various time series forecasting methods, from preprocessing to advanced neural networks, highlighting recent developments, methodologies, and open research directions in the field.

Contribution

It provides an extensive, unified overview of traditional and deep learning models for time series forecasting, including methodological advancements and future research avenues.

Findings

Coverage of three major linear models and two nonlinear extensions

Presentation of six categories of deep neural networks for forecasting

Discussion of new research directions and publicly available software

Abstract

Time series modeling for predictive purpose has been an active research area of machine learning for many years. However, no sufficiently comprehensive and meanwhile substantive survey was offered so far. This survey strives to meet this need. A unified presentation has been adopted for entire parts of this compilation. A red thread guides the reader from time series preprocessing to forecasting. Time series decomposition is a major preprocessing task, to separate nonstationary effects (the deterministic components) from the remaining stochastic constituent, assumed to be stationary. The deterministic components are predictable and contribute to the prediction through estimations or extrapolation. Fitting the most appropriate model to the remaining stochastic component aims at capturing the relationship between past and future values, to allow prediction. We cover a sufficiently broad spectrum of models while nonetheless offering substantial methodological developments. We describe three major linear parametric models, together with two nonlinear extensions, and present five categories of nonlinear parametric models. Beyond conventional statistical models, we highlight six categories of deep neural networks appropriate for time series forecasting in nonlinear framework. Finally, we enlighten new avenues of research for time series modeling and forecasting. We also report software made publicly available for the models presented.