CITATION.cff

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title: "CryptoGAT: Are Time Series Models Effective for Cryptocurrency Forecasting?"
authors:
  - family-names: Peng
    given-names: Yu
  - family-names: Khushi
    given-names: Matloob
  - family-names: Poon
    given-names: Josiah
year: 2026
doi: 10.48550/arXiv.2606.27670
url: "https://arxiv.org/abs/2606.27670"
repository-code: "https://github.com/FanBroWell/CryptoGAT"
abstract: "CryptoGAT studies whether time series models are effective for cryptocurrency forecasting and proposes a graph attention approach for cross-asset cryptocurrency prediction."
keywords:
  - cryptocurrency forecasting
  - graph attention network
  - time series
  - financial machine learning
  - AI for finance