Template-based Time series generation with Loom
Lars Kegel, Martin Hahmann, Wolfgang Lehner · 2016
Time series analysis and forecasting are important tech-niques for decision-making in many domains. They are typ-ically evaluated on given sets of time series that have a con-stant size and specified characteristics. Synthetic datasets are relevant because they are flexible in both size and charac-teristics. In this demo, we present our prototype Loom, that generates datasets with respect to the user’s configuration of categorical information and time series characteristics. The prototype allows for comparison of different analysis tech-niques.