Area2Area forecasting: Looser constraints, better predictions

Yuning Wei, Li Shen, Yangzhu Wang, Huaxin Qiu

Abstract: The advent of deep learning and neural network has revolutionized the study of time series forecasting. Diverse forecasting networks seem to achieve more promising performances than traditional forecasting models especially when handling complicated and non-linear conditions. However, most of forecasting networks are built upon Seq2Seq model, which means that they pursue the unique forecasting result when given certain input sequence. However, due to the natural noises, tiny errors and distribution shifts in real-world time series, Seq2Seq models are vulnerable to over-fitting problem. Based on these observations, we propose Area2Area forecasting formula containing Causal Sequence-wise Contrastive Learning (CSCL) and Area Loss (AL) mechanisms to loosen the forecasting constraint and alleviate over-fitting problem. CSCL utilizes contrastive learning technique to transform the input sequence into an input Area while AL modifies the loss function to transform the prediction sequence into a prediction Area. We additionally propose a novel encoder network Temporal Efficient Layer Aggregation Network (Temporal ELAN). Extensive experiments on six datasets and nine baselines demonstrate that Area2Area forecasting is literally capable of alleviating the over-fitting problem of existing Seq2Seq forecasting networks. The source code is released on https://github.com/OrigamiSL/A2A.