  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.
