{
  "id": "2307.03756",
  "version": "v1",
  "published": "2023-07-06T15:01:58.000Z",
  "updated": "2023-07-06T15:01:58.000Z",
  "title": "FITS: Modeling Time Series with $10k$ Parameters",
  "authors": [
    "Zhijian Xu",
    "Ailing Zeng",
    "Qiang Xu"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain. By discarding high-frequency components with negligible impact on time series data, FITS achieves performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks, while having a remarkably compact size of only approximately $10k$ parameters. Such a lightweight model can be easily trained and deployed in edge devices, creating opportunities for various applications. The anonymous code repo is available in: \\url{https://anonymous.4open.science/r/FITS}",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-07-06T15:01:58.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "modeling time series",
      "parameters",
      "directly process raw time-domain data",
      "time series analysis",
      "complex frequency domain"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}