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Meta-learning for time series forecasting and forecast combination.

Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73 (10-12), 2006-2016.

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Neurocomputing_Lemke_Gabrys_2009_accepted.pdf - Submitted Version


DOI: doi:10.1016/j.neucom.2009.09.020


In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature pool describing both the time series and the ensemble of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a ranking-based combination of methods over simple model selection approaches.

Item Type:Article
Uncontrolled Keywords:Forecasting, Forecast combination, Time series, Time series features, Meta-learning, Diversity
Group:Faculty of Science & Technology
ID Code:11186
Deposited By: Professor Bogdan Gabrys LEFT
Deposited On:06 Sep 2009 15:55
Last Modified:14 Mar 2022 13:24


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