mlsanityData to 7 Oct 2026

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

arXiv 2310.09751 ↗First posted 15 Oct 2023cs.LGcited by 139

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Abstract (arXiv)

Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific time series application domains, this research advocates for a unified model paradigm that transcends domain boundaries. However, learning an effective cross-domain model presents the following challenges. First, various domains exhibit disparities in data characteristics, e.g., the number of variables, posing hurdles for existing models that impose inflexible constraints on these factors. Second, the model may encounter difficulties in distinguishing data from various domains, leading to suboptimal performance in our assessments. Third, the diverse convergence rates of time series domains can also result in compromised empirical performance. To address these issues, we propose UniTime for effective cross-domain time series learning. Concretely, UniTime can flexibly adapt to data with varying characteristics. It also uses domain instructions and a Language-TS Transformer to offer identification information and align two modalities. In addition, UniTime employs masking to alleviate domain convergence speed imbalance issues. Our extensive experiments demonstrate the effectiveness of UniTime in advancing state-of-the-art forecasting performance and zero-shot transferability.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). Builds on 36 of its 48 references that are in the map. First posted on arXiv 15 Oct 2023.

Builds on

36 of 48 references.

References · 36

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24 more
  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  2. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  3. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  4. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
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  7. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  8. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  9. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  10. 2022Transformers in Time Series: A Surveycited by 489
  11. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  12. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  13. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
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  22. 2021REST: Relational Event-driven Stock Trend Forecastingcited by 30
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 36; 2025: 65; 2026: 37.

139 papers.

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  1. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  2. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
  3. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  4. 2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 120
  5. 2024Large Language Models for Time Series: A Surveycited by 113
  6. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  7. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  8. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 88
  9. 2024Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretabilitycited by 69
  10. 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Modelscited by 63
  11. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  12. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 59
38 more
  1. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  2. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  3. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 44
  4. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  5. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 37
  6. 2024SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognitioncited by 35
  7. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  8. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  9. 2025Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Modelscited by 27
  10. 2024Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecastingcited by 26
  11. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  12. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  13. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  14. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  15. 2025LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimizationcited by 21
  16. 2025MIRA: Medical Time Series Foundation Model for Real-World Health Datacited by 21
  17. 2025Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillationcited by 20
  18. 2025BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelingcited by 18
  19. 2025A Survey of AIOps in the Era of Large Language Modelscited by 18
  20. 2024Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspectivecited by 17
  21. 2024MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysiscited by 15
  22. 2024MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learningcited by 11
  23. 2025Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Eracited by 11
  24. 2025VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbonescited by 11
  25. 2025Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thoughtcited by 11
  26. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 11
  27. 2025FeDaL: Federated Dataset Learning for General Time Series Foundation Modelscited by 10
  28. 2025Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challengescited by 10
  29. 2024Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecastingcited by 9
  30. 2024PowerPM: Foundation Model for Power Systemscited by 9
  31. 2024A Mamba Foundation Model for Time Series Forecastingcited by 9
  32. 2025Vision-Enhanced Time Series Forecasting via Latent Diffusion Modelscited by 9
  33. 2025When Does Multimodality Lead to Better Time Series Forecasting?cited by 9
  34. 2026The Forecast After the Forecast: A Post-Processing Shift in Time Seriescited by 9
  35. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 8
  36. 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
  37. 2026Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservicescited by 8
  38. 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classificationcited by 7

50 most cited of 139 All 139 (JSON)