Time series refer to a series of data points indexed in time order, which can
be found in various fields, e.g., transportation, healthcare, and finance.
Accurate time series forecasting can enhance optimization planning and
decision-making support. Time series have multi-scale characteristics, i.e.,
different temporal patterns at different scales, which presents a challenge for
time series forecasting. In this paper, we propose TPRNN, a Top-down Pyramidal
Recurrent Neural Network for time series forecasting. We first construct
subsequences of different scales from the input, forming a pyramid structure.
Then by executing a multi-scale information interaction module from top to
bottom, we model both the temporal dependencies of each scale and the
influences of subsequences of different scales, resulting in a complete
modeling of multi-scale temporal patterns in time series. Experiments on seven
real-world datasets demonstrate that TPRNN has achieved the state-of-the-art
performance with an average improvement of 8.13% in MSE compared to the best
baseline.

该论文提出了一种用于时间序列预测的自上而下的金字塔式递归神经网络 TPRNN，通过构建不同尺度的子序列并执行多尺度信息交互模块，模拟了时间序列中多尺度时间模式的完整建模，实验证明 TPRNN 在七个真实世界数据集上取得了领先水平，相对最佳基准线平均提高了 8.13% 的 MSE 精度。