Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

Authors:
Francisco Martínez-Álvarez;Alicia Troncoso;José C. Riquelme
Affiliations:
Area of Computer Science, Pablo de Olavide University of Seville, Spain;Area of Computer Science, Pablo de Olavide University of Seville, Spain;Department of Computer Science, University of Seville, Spain
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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Abstract

This work aims to improve an existing time series forecasting algorithm ---LBF--- by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist many samples whose values may be specially unexpected. By the combination of frequent episodes and the LBF algorithm, the new procedure does not make better predictions over these outliers but, on the contrary, it is able to predict the apparition of such atypical samples with a great accuracy. In short, this work shows how to detect the occurrence of anomalous samples in time series improving, thus, the general forecasting scheme. Moreover, this hybrid approach has been successfully tested on electricity-related time series.