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.
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Notes
- 1.
Code and documentation are available in https://github.com/Shawn-nau/tsfmeta.
- 2.
The demonstrative codes are available at https://github.com/Shawn-nau/tsfmeta/examples
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Acknowledgements
The first author acknowledges the support of the National Natural Science Foundation of China under Grant No. 72072092; Jiangsu Social Science Foundation under Grant No. 22GLB016; and Jiangsu Philosophy and Social Science Research Major Project under Grant No. 2021SJZDA029.
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Ma, S., Fildes, R. (2023). Large-Scale Time Series Forecasting with Meta-Learning. In: Hamoudia, M., Makridakis, S., Spiliotis, E. (eds) Forecasting with Artificial Intelligence. Palgrave Advances in the Economics of Innovation and Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-35879-1_9
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