<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li Li</style></author><author><style face="normal" font="default" size="100%">Feng Li</style></author><author><style face="normal" font="default" size="100%">Kang, Yanfei</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hamoudia, Mohsen</style></author><author><style face="normal" font="default" size="100%">Makridakis, Spyros</style></author><author><style face="normal" font="default" size="100%">Spiliotis, Evangelos</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Forecasting Large Collections of Time Series: Feature-Based Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Forecasting with Artificial Intelligence: Theory and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Palgrave Advances in the Economics of Innovation and Technology</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2309.13807</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Nature Switzerland</style></publisher><pages><style face="normal" font="default" size="100%">251–276</style></pages><isbn><style face="normal" font="default" size="100%">978-3-031-35879-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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)&amp;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.</style></abstract></record></records></xml>