Feature-driven Time Series Generation

Time series data are an ubiquitous and important data source in many domains. Most companies and organizations rely on this data for critical tasks like decision-making, planning, and analytics in general. Usually, all these tasks focus on actual data representing organization and business processes. In order to assess the robustness of current systems and methods, it is also desirable to focus on time-series scenarios which represent specific time-series features. This work presents a generally applicable and easy-to-use method for the feature-driven generation of time series data. Our approach extracts descriptive features of a data set and allows the construction of a specific version by means of the modification of these features.