Implementing transfer learning across different datasets for time series forecasting

Rui Ye, Qun Dai

Abstract: 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.