Seasonal-Trend Time Series Decomposition on Graphics Processing Units

In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.