  Although transfer learning is proven to be effective in computer vision and
natural language processing applications, it is rarely investigated in
forecasting financial time series. Majority of existing works on transfer
learning are based on single-source transfer learning due to the availability
of open-access large-scale datasets. However, in financial domain, the lengths
of individual time series are relatively short and single-source transfer
learning models are less effective. Therefore, in this paper, we investigate
multi-source deep transfer learning for financial time series. We propose two
multi-source transfer learning methods namely Weighted Average Ensemble for
Transfer Learning (WAETL) and Tree-structured Parzen Estimator Ensemble
Selection (TPEES). The effectiveness of our approach is evaluated on financial
time series extracted from stock markets. Experiment results reveal that TPEES
outperforms other baseline methods on majority of multi-source transfer tasks.
