mlsanityData to 14 Oct 2026

TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness

arXiv 2506.06482 ↗First posted 6 Jun 2025cs.LGcited by 7

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Abstract (arXiv)

Time-series forecasting is an essential task with wide real-world applications across domains. While recent advances in deep learning have enabled time-series forecasting models with accurate predictions, there remains considerable debate over which architectures and design components, such as series decomposition or normalization, are most effective under varying conditions. Existing benchmarks primarily evaluate models at a high level, offering limited insight into why certain designs work better. To mitigate this gap, we propose TimeRecipe, a unified benchmarking framework that systematically evaluates time-series forecasting methods at the module level. TimeRecipe conducts over 10,000 experiments to assess the effectiveness of individual components across a diverse range of datasets, forecasting horizons, and task settings. Our results reveal that exhaustive exploration of the design space can yield models that outperform existing state-of-the-art methods and uncover meaningful intuitions linking specific design choices to forecasting scenarios. Furthermore, we release a practical toolkit within TimeRecipe that recommends suitable model architectures based on these empirical insights. The benchmark is available at: https://github.com/AdityaLab/TimeRecipe.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 54 of its 88 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

54 of 88 references.

References · 54

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  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
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38 more
  1. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  2. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  3. 2024Chronos: Learning the Language of Time Seriescited by 759
  4. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  6. 2023A decoder-only foundation model for time-series forecastingcited by 692
  7. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 674
  8. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  9. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 346
  10. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
  11. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  12. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 287
  13. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 249
  14. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 240
  15. 2024A Survey on Mixture of Experts in Large Language Modelscited by 237
  16. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 212
  17. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
  18. 2023FITS: Modeling Time Series with 10k Parameterscited by 195
  19. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
  20. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 160
  21. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 148
  22. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 116
  23. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 101
  24. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 95
  25. 2023Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecastingcited by 93
  26. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 87
  27. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 74
  28. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 63
  29. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 59
  30. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  31. 2023ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizonscited by 36
  32. 2024TSI-Bench: Benchmarking Time Series Imputationcited by 33
  33. 2024Unlocking the Power of LSTM for Long Term Time Series Forecastingcited by 29
  34. 2024Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learningcited by 27
  35. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  36. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  37. 2020VEST: Automatic Feature Engineering for Forecastingcited by 14
  38. 2025TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillationcited by 12

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecastingcited by 1
  2. 2025Tackling Time-Series Forecasting Generalization via Mitigating Concept Driftcited by 1
  3. 2026To See Far, Look Close: Evolutionary Forecasting for Long-term Time Seriescited by 1
  4. 2026In-context Pre-trained Time-Series Foundation Models adapt to Unseen Taskscited by 1
  5. 2026An Empirical Audit of Input Encoders for Multi-Channel Signal Transformerscited by 0
  6. 2026From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocolcited by 0
  7. 2026Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performancecited by 0