STL Decomposition-based Stock Price Forecasting using LSTM model

Stock price forecasting is one of the challenging research applications in time series data analysis because it is affected by numerous factors such as political news, weather, and pandemics. In addition, stock prices have several strong statistical univariate properties like unit-root property, the fat tail phenomenon, and volatility clustering. Equally important, time series in the financial market expresses strong seasonality and unpredicted components. To enhance the performance of stock price forecasting, this work proposes the method by combining the seasonal trend decomposition using the Loess (STL) method and long-short-term memory (LSTM) model. The proposed models are experimentally evaluated on 50 stocks collected from Forex, commodity, and New York Stock Exchange (NYSE) stock market. The experimental results show that LSTM-STL-price enhances the forecasting performance compared to single LSTM model.