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Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

 

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Author: Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2009
Published in: Advances in Intelligent Data Analysis VIII, Lecture Notes in Computer Science, Volume 5772, pp 357-368
Document type: Chapter of Book
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.
Size: 208.5Kb
Format: PDF

URI: http://hdl.handle.net/11441/40525

DOI: http://dx.doi.org/10.1007/978-3-642-03915-7_31

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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