A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition

@article{Yang2021ARA,
  title={A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition},
  author={Linxiao Yang and Qingsong Wen and Bo Yang and Liang Sun},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={5085-5089}
}
  • Linxiao Yang, Qingsong Wen, +1 author Liang Sun
  • Published 6 June 2021
  • Computer Science, Mathematics, Engineering
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Many real-world time series exhibit multiple seasonality with different lengths. The removal of seasonal components is crucial in numerous applications of time series, including forecasting and anomaly detection. However, many seasonal-trend decomposition algorithms suffer from high computational cost and require a large amount of data when multiple seasonal components exist, especially when the periodic length is long. In this paper, we propose a general and efficient multi-scale seasonal… Expand

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