mlsanityData to 7 Oct 2026

Data Augmentation in Time Series Forecasting through Inverted Framework

arXiv 2507.11439 ↗First posted 15 Jul 2025cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

21 of 30 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  7. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  10. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  11. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  12. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
9 more
  1. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  2. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  3. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  4. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 241
  5. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  6. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  7. 2024TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecastingcited by 63
  8. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 54
  9. 2020Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentationcited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Datacited by 0