本文提出一种基于时间序列模式相似性的新型预测方法。首先通过聚类技术对数据集样本进行分组和标记，随后利用历史数据中与待预测日期前的模式序列相匹配的数据点进行平均，以此作为预测值。该方法的独特之处在于仅使用与每个模式相关的标签来预测时间序列的未来行为，直到预测过程的最后一步才引入实际数值。实验结果表明，对于多个能源时间序列的预测效果显著优于近期发表的技术。
ABSTRACT
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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