@inbook {LiL2023ForecastingLarge,
	title = {Forecasting Large Collections of Time Series: Feature-Based Methods},
	booktitle = {Forecasting with Artificial Intelligence: Theory and Applications},
	series = {Palgrave Advances in the Economics of Innovation and Technology},
	year = {2024},
	pages = {251{\textendash}276},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	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)\&nbsp;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.},
	isbn = {978-3-031-35879-1},
	doi = {10.1007/978-3-031-35879-1_10},
	url = {http://arxiv.org/abs/2309.13807},
	author = {Li Li and Feng Li and Kang, Yanfei},
	editor = {Hamoudia, Mohsen and Makridakis, Spyros and Spiliotis, Evangelos}
}
