迁移从多个领域学习的知识以进行时间序列数据.zip

# Transfer-learning Transfer Knowledge Learned from Multiple Domains for Time-series Data Prediction • Applied a deep-learning based method to enable the knowledge transfer between time-series data, learned the feature space for both source and target domains based on Autoencoder. • Input the training data to multiple feature space to extract features in different feature structure learned from multiple domains, built model with NN on Keras to train the model with extracted features. • Predicted the time-series data (stock price) %30 more accurate than the baseline method.