HDResNet: Hierarchical-Decomposition Residual Network for Hierarchical Time Series Forecasting

Hierarchical time series forecasting plays a vital role in practical applications. To guarantee the accuracy and coherence simultaneously for the prediction, most of the classical methods tackle this problem with two-stage models, which may not obtain optimal predictions due to the complex computational process. Although the majority of end-to-end deep learning models were proposed to avoid the shortage of traditional methods, most existing works stack the shallow neural network directly which caused degradation issues possibly and decreased the prediction accuracy. In this paper, we draw on the idea of deep residual networks (ResNet) and the top-down method to form an end-to-end hierarchical decomposition residual network (HDResNet), which could decrease the computation cost of prediction and improve the forecasting outcome. For each block of our model, we firstly employ the Long Short-Term Memory (LSTM) layer and linear layer to obtain two decompositions, the residual forecasting and proportion forecasting. Then, a functional layer which is based on the top-down approach is proposed, to update the residual forecasting of children nodes and decrease the noise transmission. Moreover, considering the stability at different levels, a weighted loss function is proposed based on the coefficient of variation for each level. Through three public datasets, we prove that our model has a superior performance to the existing hierarchical forecasting models.