Parallel Techniques for Variable Size Segmentation of Time Series Datasets.

Given the high data volumes in time series applications, or simply the need for fast response times, it is usually necessary to rely on alternative, shorter representations of these series, usually with loss. This incurs approximate comparisons of time series where precision is a major issue. In this paper, we propose a new parallel approach for segmenting time series before their transformation into symbolic representations. It can reduce significantly the error incurred by possible splittings at different steps of the representation calculation, by taking into account the sum of squared errors (SSE). This is particularly useful for time series similarity search, which is the core of many data analytics tasks. We provide theoretical guarantees on the lower bound of similarity measures, and our experiments illustrate that our technique can improve significantly the time series representation quality.