AN INTERPRETABLE DEEP LEARNING BASED DECISION SUPPORT SYSTEM FOR STOCK FORECASTING AND INVESTMENT RECOMMENDATION USING TEMPORAL FUSION TRANSFORMER

The stock market has rapid changes caused by economic conditions, company performance and investor behavior. Because of this, stock price predictions and correct investment decisions are hardly achieved. Investors today use computer-based systems to study large volumes of financial data and find useful patterns barely perceived using traditional methods. However, most existing stock prediction and recommendation systems that have some major problems. Many systems use only technical indicators or only fundamental data, while not both together. Some models cannot properly capture both the short-term and long-term price movements of the stock. Moreover, many of these deep learning models behave like black boxes and fail to explain clearly why any stock is recommended to be on buy, sell or hold. This therefore compromises trust in their prediction capabilities, especially those specifically for highly volatile markets. In this paper, we try to solve those problems by proposing a new stock forecasting and recommendation system based on the Temporal Fusion Transformer (TFT), combining historical stock prices, technical indicators and fundamental financial data in one framework. It can learn both short-term and long-term trends and show which factor is most important for the prediction. After predicting the price and other financial indicators, our model extends TFT into a decision-support system by embedding financial ratio analysis and recommendation logic within the forecasting pipeline.