Implementing transfer learning across different datasets for time series forecasting

作者：

Highlights：

• DTr-CNN implements time series forecasting transfer learning across different datasets.

• DTr-CNN alleviates the problem of lacking labeled target data in time series prediction.

• Instead of only fine-tuning, DTr-CNN embeds the transfer phase into feature learning.

• DTr-CNN incorporates DTW and JS divergence to evaluate similarity between datasets.

• DTr-CNN takes advantages of CNN and applies it to forecasting problems.

摘要

•DTr-CNN implements time series forecasting transfer learning across different datasets.•DTr-CNN alleviates the problem of lacking labeled target data in time series prediction.•Instead of only fine-tuning, DTr-CNN embeds the transfer phase into feature learning.•DTr-CNN incorporates DTW and JS divergence to evaluate similarity between datasets.•DTr-CNN takes advantages of CNN and applies it to forecasting problems.

论文关键词：Time series prediction,Deep learning,Transfer learning,Convolutional neural network (CNN)

论文评审过程：Received 26 December 2019, Revised 25 July 2020, Accepted 24 August 2020, Available online 25 August 2020, Version of Record 29 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107617