{
  "annotation_id": "fc52f381-cd93-4a5f-a914-fd1a621d9dcf",
  "date_created": "2026-02-17T05:53:16.297000Z",
  "date_modified": "2026-02-17T05:53:16.297000Z",
  "file_hash": "4d8c7e5219e94aa30765a711b32e55760b1e47269b657dfab918e9d2ac3d1219",
  "private": false,
  "record": {
    "abstract": "Forecasting time series with extreme events is critical yet challenging due to their high variance, irregular dynamics, and sparse but high-impact nature. While existing methods excel in modeling dominant regular patterns, their performance degrades significantly during extreme events, constituting the primary source of forecasting errors in real-world applications. Although some approaches incorporate auxiliary signals to improve performance, they still fail to capture extreme events\u0027 complex temporal dynamics. To address these limitations, we propose M$^2$FMoE, an extreme-adaptive forecasting model that learns both regular and extreme patterns through multi-resolution and multi-view frequency modeling. It comprises three modules: (1) a multi-view frequency mixture-of-experts module assigns experts to distinct spectral bands in Fourier and Wavelet domains, with cross-view shared band splitter aligning frequency partitions and enabling inter-expert collaboration to capture both dominant and rare fluctuations; (2) a multi-resolution adaptive fusion module that hierarchically aggregates frequency features from coarse to fine resolutions, enhancing sensitivity to both short-term variations and sudden changes; (3) a temporal gating integration module that dynamically balances long-term trends and short-term frequency-aware features, improving adaptability to both regular and extreme temporal patterns. Experiments on real-world hydrological datasets with extreme patterns demonstrate that M$^2$FMoE outperforms state-of-the-art baselines without requiring extreme-event labels.",
    "arxiv_id": "2601.08631",
    "authors": [
      "Yaohui Huang",
      "Runmin Zou",
      "Yun Wang",
      "Laeeq Aslam",
      "Ruipeng Dong"
    ],
    "categories": [
      "cs.LG",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting",
    "url": "https://arxiv.org/abs/2601.08631",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "32e92c96-a3ea-4a58-81b7-3aea0bb15391",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}