Forecasting large collections of time series: feature-based methods
This is an incremental review chapter for practitioners in economics and forecasting domains.
The paper tackles the challenge of forecasting large collections of time series by reviewing feature-based methods, such as model selection and combination, to address the variability in performance across different series, without reporting specific numerical results.
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.