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Forecasting Large Collections of Time Series: Feature-Based Methods

In: Forecasting with Artificial Intelligence

Author

Listed:
  • Li Li

    (University of Science & Technology Beijing)

  • Feng Li

    (Central University of Finance and Economics)

  • Yanfei Kang

    (Beihang University)

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.

Suggested Citation

  • Li Li & Feng Li & Yanfei Kang, 2023. "Forecasting Large Collections of Time Series: Feature-Based Methods," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 251-276, Palgrave Macmillan.
    • Handle: RePEc:pal:paiecp:978-3-031-35879-1_10
    DOI: 10.1007/978-3-031-35879-1_10
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