LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
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...
| Autores: | , , , |
|---|---|
| Formato: | capítulo de livro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/40507 |
| Acesso em linha: | http://hdl.handle.net/11441/40507 https://doi.org/10.1109/ICDM.2008.129 |
| Access Level: | acceso abierto |
| Palavra-chave: | Clustering Forecasting Neighbourhood Time series |
| Resumo: | 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. |
|---|