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STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy

arXiv 2605.25943 ↗First posted 25 May 2026cs.LGcited by 0

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Abstract (arXiv)

Recent research in time series forecasting frequently investigates the integration of textual and visual modalities with numerical models to better navigate non-stationary environments. Despite delivering solid numerical results, existing multi-modal approaches usually encounter a dilemma: prioritizing the minimization of average errors can result in excessively smooth forecasts that overlook essential fluctuations. To resolve this limitation, we introduce STaT, an innovative multimodal architecture for Symbolic-Temporal-Textual Alignment, which seamlessly unites three synergistic modalities. Specifically, the symbolic modality converts continuous time series into discrete tokens, facilitating the accurate identification of structural patterns and turning points; the temporal modality extracts inherent sequential dependencies; and the textual modality leverages domain semantics to steer the macroscopic forecasting trends. Comprehensive evaluations on eight real-world benchmarks indicate that STaT delivers exceptional performance, enhancing conventional magnitude indicators by up to 8.9% while simultaneously decreasing shape distortion by up to 8.5%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 25 May 2026.

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19 of 43 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
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  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
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  10. 2022Earthformer: Exploring Space-Time Transformers for Earth System Forecastingcited by 152
  11. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 103
  12. 2019Shape and Time Distortion Loss for Training Deep Time Series Forecasting Modelscited by 70
7 more
  1. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 63
  2. 2025Time-o1: Time-Series Forecasting Needs Transformed Label Alignmentcited by 22
  3. 2020ABBA: Adaptive Brownian bridge-based symbolic aggregation of time seriescited by 13
  4. 2022An efficient aggregation method for the symbolic representation of temporal datacited by 7
  5. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
  6. 2025T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusioncited by 7
  7. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4

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