Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

Understand

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.