Categories

Our article, “Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis”, has been accepted for publication in the International Journal of Data and Information Quality. As time series data are crucial yet often limited or confidential, we focus in this work on time series synthesis an important alternative. Despite the availability of numerous synthesis methods, evaluating their quality remains challenging. Our comprehensive survey defines what constitutes “good” synthesis and proposes a systematic evaluation procedure. This work aims to drive rigorous and reproducible research in the field of time series synthesis.