Timeseries Based Deep Hybrid Transfer Learning Frameworks: A Case Study of Electric Vehicle Energy Prediction

The problem of limited labelled data availability causes under-fitting, which negatively affects the development of accurate time series based prediction models. Two-hybrid deep neural network architectures, namely the CNN-BiLSTM and the Conv-BiLSTM, are proposed for time series based transductive transfer learning and compared to the baseline CNN model. The automatic feature extraction abilities of the encoder CNN module combined with the superior recall of both short and long term sequences by the decoder LSTM module have shown to be advantageous in transfer learning tasks. The extra ability to process in both forward and backward directions by the proposed models shows promising results to aiding transfer learning. The most consistent transfer learning strategy involved freezing both the CNN and BiLSTM modules while retraining only the fully connected layers. These proposed hybrid transfer learning models were compared to the baseline CNN transfer learning model and newly created hybrid models using the R2, MAE and RMSE metrics. Three electrical vehicle data-sets were used to test the proposed transfer frameworks. The results favour the hybrid architectures for better transfer learning abilities relative to utilising the baseline CNN transfer learning model. This study offers guidance to enhance time series-based transfer learning by using available data sources.