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Energy Time Series Forecasting Based on Pattern Sequence Similarity
August 2011 (vol. 23 no. 8)
pp. 1230-1243
Francisco Martínez-Álvarez, Pablo de Olavide University, Seville
Alicia Troncoso, Pablo de Olavide University, Seville
José C. Riquelme, Pablo de Olavide University, Seville
Jesús S. Aguilar-Ruiz, Pablo de Olavide University, Seville
This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.

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Index Terms:
Time series, forecasting, patterns.
Francisco Martínez-Álvarez, Alicia Troncoso, José C. Riquelme, Jesús S. Aguilar-Ruiz, "Energy Time Series Forecasting Based on Pattern Sequence Similarity," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1230-1243, Aug. 2011, doi:10.1109/TKDE.2010.227
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