mlsanityData to 7 Oct 2026

EMTSF:Extraordinary Mixture of SOTA Models for Time Series Forecasting

arXiv 2510.23396 ↗First posted 27 Oct 2025cs.CL cs.AIcited by 0

On the map: Mixture-of-experts → MoE for time series and graphs

Abstract (arXiv)

The immense success of the Transformer architecture in Natural Language Processing has led to its adoption in Time Se ries Forecasting (TSF), where superior performance has been shown. However, a recent important paper questioned their effectiveness by demonstrating that a simple single layer linear model outperforms Transformer-based models. This was soon shown to be not as valid, by a better transformer-based model termed PatchTST. More re cently, TimeLLM demonstrated even better results by repurposing a Large Language Model (LLM) for the TSF domain. Again, a follow up paper challenged this by demonstrating that removing the LLM component or replacing it with a basic attention layer in fact yields better performance. One of the challenges in forecasting is the fact that TSF data favors the more recent past, and is sometimes subject to unpredictable events. Based upon these recent insights in TSF, we propose a strong Mixture of Experts (MoE) framework. Our method combines the state-of-the-art (SOTA) models including xLSTM, en hanced Linear, PatchTST, and minGRU, among others. This set of complimentary and diverse models for TSF are integrated in a Trans former based MoE gating network. Our proposed model outperforms all existing TSF models on standard benchmarks, surpassing even the latest approaches based on MoE frameworks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 30 of its 42 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

30 of 42 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts14 Oct
  2. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts24 Sep

Other references · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  6. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  7. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  8. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  9. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  10. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  11. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  12. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
16 more
  1. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  2. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  4. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  5. 2024xLSTM: Extended Long Short-Term Memorycited by 320
  6. 2023From Sparse to Soft Mixtures of Expertscited by 245
  7. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 241
  8. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  9. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  10. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 105
  11. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  12. 2020Machine Learning Advances for Time Series Forecastingcited by 49
  13. 2024Were RNNs All We Needed?cited by 36
  14. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecastingcited by 29
  15. 2024WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecastingcited by 25
  16. 2024xLSTMTime : Long-term Time Series Forecasting With xLSTMcited by 13

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  1. 2026TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
  2. 2025A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
  3. 2025Fusing Large Language Models with Temporal Transformers for Time Series Forecasting
  4. 2026Rethinking the Role of LLMs in Time Series Forecasting
  5. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
  6. 2025BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
  7. 2024TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models
  8. 2025Why Do Transformers Fail to Forecast Time Series In-Context?