  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.
