ForeNet: Unlocking Long-Term Series Forecasting in High-Dimensional Scenario via Forest Structure

Facing the key challenge in multivariate long-term series prediction, namely effectively modeling the long-term dependencies among high-dimensional variables, we propose an innovative forest network (ForeNet). Firstly, we construct a polytree, which takes variable as leaf node, convolution as edge, and progressive fusion as the root node. Polytree models the dependencies among variables from the bottom up, adopting a local-to-global progressive learning strategy. Moreover, polytree embeds the entire sequence as input channels for convolution, allowing interactions across arbitrary time steps between variables. Thereby, polytree could model long-term dependencies. Then, we construct multiple polytrees with varying branching factors, utilizing the self-attention mechanism to assign weights and combine polytrees into a forest. Forest adopts an ensemble learning strategy to capture complex patterns hidden under high-dimensional variables. Combining progressive and ensemble strategies, ForeNet could effectively address the key challenge mentioned at the beginning. Extensive experiments show that ForeNet could reduce the average MSE by up to 11.53% compared to the baseline, which verifies the effectiveness of ForeNet. Source code is available at: https://github.com/lxy-PhD2022/ForeNet