LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Extracting trees of quantitative serial episodes
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Pattern recognition to forecast seismic time series
Expert Systems with Applications: An International Journal
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This work proposes a novel general-purpose forecasting algorithm. It first extracts patterns from time series using the information provided by certain clustering techniques, which are applied as a first step of the approach. Moreover, the occurrence of data with especially unexpected values (outliers) is also addressed in this work. To deal with these outliers, a new hybrid methodology has been proposed, by inserting and adapting an existing approach based on the discovery of frequent episodes in sequences in the general scheme of prediction.