Deep learning for time series forecasting: a survey

Abstract Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, numerous new models have emerged in the field of time series forecasting in recent years. However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets. To address this gap, in this review, we comprehensively…

• NN
  National Natural Science Foundation of China

  Award: 62072409
• NS
  Natural Science Foundation of Zhejiang Province

  Award: LR21F020003