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LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series

 

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Opened Access LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
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Author: Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2008
Published in: 2008 Eighth IEEE International Conference on Data Mining, Dec. 2008, pp. 453 - 461
Document type: Chapter of Book
Abstract: A new approach is presented in this work with the aim of predicting time series behaviors. A previous labeling of the samples is obtained utilizing clustering techniques and the forecasting is applied using the information provided by the clustering. Thus, the whole data set is discretized with the labels assigned to each data point and the main novelty is that only these labels are used to predict the future behavior of the time series, avoiding using the real values of the time series until the process ends. The results returned by the algorithm, however, are not labels but the nominal value of the point that is required to be predicted. The algorithm based on labeled (LBF) has been tested in several energy-related time series and a notable improvement in the prediction has been achieved.
Size: 424.7Kb
Format: PDF

URI: http://hdl.handle.net/11441/40507

DOI: http://dx.doi.org/10.1109/ICDM.2008.129

This work is under a Creative Commons License: 
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