You May Not Need Order in Time Series Forecasting

Zhang, Yunkai, Jiang, Qiao, Li, Shurui, Jin, Xiaoyong, Ma, Xueying, Yan, Xifeng

arXiv.org Machine Learning 

Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies among time steps in finer granularity. We achieve competitive results compared to the state-of-the-art on real-world datasets.