Large-Scale Time Series Forecasting with Meta-Learning

Shaohui Ma () and Robert Fildes ()
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Shaohui Ma: Nanjing Audit University
Robert Fildes: Lancaster University

Chapter Chapter 9 in Forecasting with Artificial Intelligence, 2023, pp 221-250 from Palgrave Macmillan

Abstract: Abstract Many industrial applications concern the forecasting of large numbers of time series. In such circumstances, selecting a proper prediction model for a time series can no longer depend on the forecaster's experience. The interest in time series forecasting with meta-learning has been growing in recent years, as it is a promising method for automatic forecasting model selection and combination. In this chapter, we briefly review the current development of meta-learning methods in time series forecasting, summarize a general meta-learning framework for time series forecasting, and discuss the key elements of establishing an effective meta-learning system. We then introduce a meta-learning python library named ‘tsfmeta’, which aims to make meta-learning available for researchers and time series forecasting practitioners in a unified, easy-to-use framework. The experimental evaluation of the ‘tsfmeta’ on two open-source datasets further shows the promising performance of meta-learning on time series forecasting in various disciplines. We also offer suggestions for further academic research in time series forecasting with meta-learning.

Keywords: Forecasting; Time series; Meta-learning; Machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:pal:paiecp:978-3-031-35879-1_9

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DOI: 10.1007/978-3-031-35879-1_9

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