Temporal Fusion Transformers와 심층 학습 방법을사용한 다층 수평 시계열 데이터 분석

논문상세정보
' Temporal Fusion Transformers와 심층 학습 방법을사용한 다층 수평 시계열 데이터 분석' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Deep Learning
  • Multi-horizon Forecasting
  • Time Series
  • multivariate data analysis
  • neural networks
  • 깊은 인공 신경망
  • 다변량 데이터 분석
  • 다층 수평 예측
  • 시계열 분석
  • 심층학습
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' Temporal Fusion Transformers와 심층 학습 방법을사용한 다층 수평 시계열 데이터 분석' 의 참고문헌

  • Wearable fall detector using recurrent neural networks
  • Temporal fusion transformers for interpretable multi-horizon time series forecasting
  • Recurrent neural networks for time series forecasting : Current status and future directions
  • Multihorizon time series forecasting with temporal attention learning
    C. Fan [2019]
  • Informer: Beyond efficient transformer for long sequence time-series forecasting
    H. Zhou [2021]
  • DeepAR : Probabilistic forecasting with autoregressive recurrent networks
    D. Salinas [2020]
  • Deep learning based fall detection algorithms for embeded systems, smartwatches, and IoT devices using accelerometers
    D. Kraft [2020]
  • DSTP-RNN : A dualstage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction
    Y. Liu [2020]
  • Corporacion favorita grocery sales forecasting competition
    C. Favorita [2009]
  • Carriage Services, Inc.
  • Assessing Beijing’s PM2. 5pollution : Severity, weather impact
    X. Liang [2015]
  • A multi-horizon quantile recurrent forecaster