TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting
This work addresses the problem of accurate forecasting for applications in fields like transportation and finance, but it appears incremental as it builds on existing neural network approaches with a novel architectural modification.
The paper tackled the challenge of multi-scale temporal patterns in time series forecasting by proposing TPRNN, a top-down pyramidal recurrent neural network, which achieved state-of-the-art performance with an average improvement of 8.13% in MSE across seven real-world datasets.
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.