针对时间序列预测中反向框架的缺陷，提出实时数据增强方法DAIF。
Data Augmentation in Time Series Forecasting through Inverted Framework
- 在反向序列到序列框架上设计实时数据增强策略
- 在多数据集上显著提升模型预测性能，缓解噪声干扰
- 适合做多变量时间序列预测的算法研究者使用
目前，iTransformer 是多变量时间序列（MTS）预测中最受欢迎且有效的模型之一。得益于其反向框架，iTransformer 能有效捕捉变量间的相关性。然而，该反向框架仍存在局限：削弱了时间依赖信息，并在变量相关性不显著时引入噪声。为此，我们提出一种针对反向框架的新颖数据增强方法——DAIF。与以往方法不同，DAIF 是首个专为 MTS 预测中反向框架设计的实时增强方法。我们首先定义了反向序列到序列框架的结构，随后提出两种策略：频率滤波与跨变差补丁。在多个数据集和反向模型上的实验验证了 DAIF 的有效性。
原文摘要 · Abstract (English)
Currently, iTransformer is one of the most popular and effective models for multivariate time series (MTS) forecasting. Thanks to its inverted framework, iTransformer effectively captures multivariate correlation. However, the inverted framework still has some limitations. It diminishes temporal interdependency information, and introduces noise in cases of nonsignificant variable correlation. To address these limitations, we introduce a novel data augmentation method on inverted framework, called DAIF. Unlike previous data augmentation methods, DAIF stands out as the first real-time augmentation specifically designed for the inverted framework in MTS forecasting. We first define the structure of the inverted sequence-to-sequence framework, then propose two different DAIF strategies, Frequency Filtering and Cross-variation Patching to address the existing challenges of the inverted framework. Experiments across multiple datasets and inverted models have demonstrated the effectiveness of our DAIF.
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