Professor Yanfei Kang gave a keynote talk on “Feature-based time series forecasting” on SFE-TSDM Workshop at 21st IEEE International Conference on Data Mining (IEEE ICDM 2021).
The workshop on Systematic Feature Engineering for Time-Series Data Mining is organized as part of the 21st IEEE International Conference on Data Mining, which will be held from 7-10 December 2021 in Auckland, New Zealand.
Time series are one of the most common data types in science, engineering, medicine, and economics. In many applications, not the sequential time-series values themselves, but their properties (e.g., autocorrelation structure, entropy, outliers, etc.) are important for analyzing and understanding the respective systems from which they have been recorded. These time-series features have the benefit that they are interpretable, provide valuable insights for domain experts and support explainable machine-learning models.