A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction

In the last two decades, with the rise of the Data Mining process, there is an increasing interest in the adaptation of Machine Learning methods to support Time Series non-parametric modeling and prediction. The non-parametric temporal data modeling can be performed according to local and global approaches. The most of the local prediction data strategies are based on the k-Nearest Neighbor (kNN) learning method. In this paper we propose a modification of the kNN algorithm for Time Series prediction. Our proposal differs from the literature by incorporating three techniques for obtaining amplitude and offset invariance, complexity invariance, and treatment of trivial matches. We evaluate the proposed method with six complexity measures, in order to verify the impact of these measures in the projection of the future values. Besides, we face our method with two Machine Learning regression algorithms. The experimental comparisons were performed using 55 data sets, which are available at the ICMC-USP Time Series Prediction Repository. Our results indicate that the developed method is competitive and the use of a complexity-invariant distance measure generally improves the predictive performance.