Financial time series data pattern detection, forecasting and its application - Ooi, Yuxuan (Nanyang Technological University, 2021)
This paper studies the latest techniques for financial time series forecasting by extending the existing work. In addition to historical stock data, sentiment analysis and signal analysis methods are applied to simulate the real-world factors that could potentially affect the stock trends. Three LSTM-based models with varied input features and architectures were trained and tested with different popular tech stocks. The experiment result shows that adding a new dimension of public sentiment helps to improve the prediction model to forecast a closing price trend that follows closely to the actual price. Furthermore, this paper proposes a trading platform that applies the prediction model built as a real-world use case. A trading algorithm is proposed to utilize the forecasted results to provide an auto-trading service and serves as the core service of the platform. The platform comes in the form of mobile application and is equipped with useful functionalities with the goal of capturing the market.