Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting


  • Qiquan Shi Huawei Noah's Ark Lab
  • Jiaming Yin Tongji University
  • Jiajun Cai The University of Hong Kong
  • Andrzej Cichocki The Skolkovo Institute of Science and Technology
  • Tatsuya Yokota Nagoya Institute of Technology
  • Lei Chen Huawei Noah's Ark Lab
  • Mingxuan Yuan Huawei Noah's Ark Lab
  • Jia Zeng Huawei Noah's Ark Lab



This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same time, the generalized tensor Autoregressive Integrated Moving Average (ARIMA) is explicitly used on consecutive core tensors to predict future samples. In this manner, the proposed approach tactically incorporates the unique advantages of MDT tensorization (to exploit mutual correlations) and tensor ARIMA coupled with low-rank Tucker decomposition into a unified framework. This framework exploits the low-rank structure of block Hankel tensors in the embedded space and captures the intrinsic correlations among multiple TS, which thus can improve the forecasting results, especially for multiple short time series. Experiments conducted on three public datasets and two industrial datasets verify that the proposed BHT-ARIMA effectively improves forecasting accuracy and reduces computational cost compared with the state-of-the-art methods.




How to Cite

Shi, Q., Yin, J., Cai, J., Cichocki, A., Yokota, T., Chen, L., Yuan, M., & Zeng, J. (2020). Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5758-5766.



AAAI Technical Track: Machine Learning