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Time series refer to a series of data points indexed in time order, which canbe found in various fields, e.g., transportation, healthcare, and finance.Accurate time series forecasting can enhance optimization planning anddecision-making support. Time series have multi-scale characteristics, i.e.,different temporal patterns at different scales, which presents a challenge fortime series forecasting. In this paper, we propose TPRNN, a Top-down PyramidalRecurrent Neural Network for time series forecasting. We first constructsubsequences of different scales from the input, forming a pyramid structure.Then by executing a multi-scale information interaction module from top tobottom, we model both the temporal dependencies of each scale and theinfluences of subsequences of different scales, resulting in a completemodeling of multi-scale temporal patterns in time series. Experiments on sevenreal-world datasets demonstrate that TPRNN has achieved the state-of-the-artperformance with an average improvement of 8.13% in MSE compared to the bestbaseline.

TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting