Analysing Forecasting of Stock Prices: An Explainable AI Approach
The paper explores using deep learning and explainable AI for accurate stock price prediction and model comparison.
What it examines
This paper explores the use of Explainable AI (XAI) techniques in time series data analysis for stock market price forecasting. It aims to compare different models, improve current techniques, and provide insights into the factors affecting stock market predictions using deep learning models.
What it concludes
The research demonstrates that combining deep learning with XAI techniques can improve stock market predictions and provide valuable insights. Potential applications include financial forecasting and investment strategies. Future research could explore other XAI methods and apply the model to different datasets.
Evidence objects
The research demonstrates that combining deep learning with XAI techniques can improve stock market predictions and provide valuable insights. Potential applications include financial forecasting and investment strategies. Future research could explore other XAI methods and apply the model to different datasets.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented.
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