Forecasting Large Collections of Time Series: Feature-Based Methods

Li Li (), Feng Li () and Yanfei Kang ()
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Li Li: University of Science & Technology Beijing
Yanfei Kang: Beihang University

Chapter Chapter 10 in Forecasting with Artificial Intelligence, 2023, pp 251-276 from Palgrave Macmillan

Abstract: 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 changesChange(s) 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 implementationsImplementation.

Date: 2023
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DOI: 10.1007/978-3-031-35879-1_10

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