Improving Performance of Similarity Measures for Uncertain Time Series using Preprocessing Techniques

We study the impact of preprocessing techniques on performance and effectiveness of the similarity measures for uncertain time series. Some existing work on uncertain time series use the same similarity measures developed for standard time series, to which we refer as traditional similarity measures. More recently, a number of new similarity measures have been proposed for uncertain time series, to which we refer as uncertain similarity measures. However, they have been shown not to be as effective as the traditional measures. In this work, we show that the performance of uncertain similarity measures can be improved through preprocessing techniques. We establish this through extensive experiments using the UCR benchmark data. Our results in fact indicate that the uncertain similarity measures together with preprocessing outperform the traditional similarity measures.