mlsanityData to 14 Oct 2026

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting

arXiv 2508.07490 ↗First posted 10 Aug 2025cs.LG stat.MLcited by 1

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

Abstract (arXiv)

Deep learning approaches are increasingly relevant for time series forecasting tasks. Methods such as N-BEATS, which is built on stacks of multilayer perceptrons (MLPs) blocks, have achieved state-of-the-art results on benchmark datasets and competitions. N-BEATS is also more interpretable relative to other deep learning approaches, as it decomposes forecasts into different time series components, such as trend and seasonality. In this work, we present N-BEATS-MOE, an extension of N-BEATS based on a Mixture-of-Experts (MoE) layer. N-BEATS-MOE employs a dynamic block weighting strategy based on a gating network which allows the model to better adapt to the characteristics of each time series. We also hypothesize that the gating mechanism provides additional interpretability by identifying which expert is most relevant for each series. We evaluate our method across 12 benchmark datasets against several approaches, achieving consistent improvements on several datasets, especially those composed of heterogeneous time series.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 21 of its 32 references that are in the map. First posted on arXiv 10 Aug 2025.

Builds on

21 of 32 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts14 Oct
  2. 2024A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction26 Sep
  3. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts24 Sep

Other references · 18

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2024Mixtral of Expertscited by 1,823
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  4. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  5. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  6. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  7. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  8. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  9. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  10. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  11. 2024Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Expertscited by 205
  12. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
6 more
  1. 2020Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globalitycited by 70
  2. 2022Criteria for Classifying Forecasting Methodscited by 48
  3. 2023Robust Mixture-of-Expert Training for Convolutional Neural Networkscited by 30
  4. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecastingcited by 29
  5. 2024Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shiftcited by 24
  6. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
  2. 2025Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
  3. 2026Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
  4. 2026MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
  5. 2025Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
  6. 2026Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
  7. 2025GateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
  8. 2024Are KANs Effective for Multivariate Time Series Forecasting?