Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation

Si Woon Lee, Ha Young Kim

Abstract: Highlights•Our model learns high-level features from super-high dimensional time-series data.•All companies’ price data in the relevant country’s open market are used as input.•The trend sampling mini-batch sampling method enhances forecasting performance.•Experimental results show that our model adapts to real-time patterns.•The model outperforms others with the same training and testing conditions.