Dish-TS：一种减轻时间序列预测中分布偏移的通用范式
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
摘要：
时间序列预测中的分布偏移是指序列分布随时间的变化，它在很大程度上阻碍了时间序列预测模型的性能。现有的工作对分布偏移的时间序列大多局限于分布的量化，更重要的是，忽略了潜在的移动之间的回望和地平线窗口。针对上述挑战，本文系统地将TSF的分布偏移分为两类。视回视窗口为输入空间，视水平窗口为输出空间，存在（i）空间内移位，即输入空间内的分布随时间推移而保持移位，以及（ii）空间间移位，即输入空间和输出空间之间的分布移位。然后，我们介绍，Dish TS，一个一般的神经范式，以减轻TSF的分布偏移。具体来说，为了更好地估计分布，我们提出了系数网络（CONET），它可以是任何神经架构，将输入序列映射到可学习的分布系数。为了减轻空间内和空间间的移位，我们将Dish-TS组织为Dual-CONET框架，以分别学习输入空间和输出空间的分布，从而自然地捕获两个空间的分布差异。此外，我们介绍了一个更有效的训练策略，顽固CONET学习。最后，我们在几个数据集上进行了广泛的实验，并结合了不同的最先进的预测模型。实验结果表明，Dish-TS一贯提高他们超过20%的平均改善。代码可用。
英文摘要：
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (CONET), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-CONET framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable CONET learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
