Data Augmentation in Time Series Forecasting through Inverted Framework

2025-07-15 · Hongming Tan, Ting Chen, Ruochong Jin, Wai Kin Chan

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.

📄 PDF Abstract BibTeX arXiv:2507.11439

Code (0)

등록된 구현이 없습니다.

Tasks

Data AugmentationTime SeriesTime Series Forecasting

Methods 이 논문이 사용한 방법론

Patching Activation patching studies the model's computation by altering its latent representations, the token embeddings in transformer-based language models, during the inference process

MTS 설명 없음

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

2026-05-18 · Masahiro Suzuki, Bohui Xia, Hiroto Yamamoto, Masanori Miyahara arxiv

Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitation in generating meaningful data. To address this limitation, we pro…

Reinforcement Learning Data Augmentation

FrAug: Frequency Domain Augmentation for Time Series Forecasting

2023-02-18 · Muxi Chen, Zhijian Xu, Ailing Zeng, Qiang Xu

Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time series DA techniques are proposed in the …

Anomaly Detection Data Augmentation Time Series Time Series Analysis+2

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

2020-08-06 · Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang 외

Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming …

Data Augmentation Dynamic Time Warping Time Series Time Series Analysis+1

IBMA: An Imputation-Based Mixup Augmentation Using Self-Supervised Learning for Time Series Data

2025-11-11 · Dang Nha Nguyen, Hai Dang Nguyen, Khoa Tho Anh Nguyen arxiv

Data augmentation in time series forecasting plays a crucial role in enhancing model performance by introducing variability while maintaining the underlying temporal patterns. However, time series data offers fewer augme…

Self-Supervised Learning Time Series Forecasting Data Augmentation

Data Augmentation Policy Search for Long-Term Forecasting

2024-05-01 · Liran Nochumsohn, Omri Azencot

Data augmentation serves as a popular regularization technique to combat overfitting challenges in neural networks. While automatic augmentation has demonstrated success in image classification tasks, its application to …

Bayesian Optimization Bilevel Optimization Data Augmentation image-classification+2