Forecasting large collections of time series: feature-based methods

TL;DR

This paper reviews feature-based methods for forecasting large collections of time series, emphasizing model selection and combination techniques that adapt to diverse data characteristics.

Contribution

It provides a comprehensive overview of current feature-based forecasting methods and discusses open-source tools, highlighting advances in handling complex, large-scale time series data.

Findings

Feature-based methods improve forecasting accuracy for diverse time series.

Open-source software facilitates the application of feature-based forecasting.

Model selection and combination strategies are key to handling complex data.

Abstract

In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting performance of different methods changes depending on the nature of the time series. When forecasting large collections of time series, two lines of approaches have been developed using time series features, namely feature-based model selection and feature-based model combination. This chapter discusses the state-of-the-art feature-based methods, with reference to open-source software implementations.