A Integrated Approach for Asset Price Forecasting via Prophet Model and Optimizing Investment Strategies through Genetic Algorithms
Keywords:
forecasting models, optimizing strategy, price prediction, gold price, oil priceAbstract
This research presents an in-depth exploration of a wide array of algorithms, techniques, methods and models used for forecasting asset values. Significantly, the study introduces an unprecedented approach, featuring a dedicated model for precise price forecasting and another for recommending optimized strategies. By assessing and contrasting the approaches and outcomes of asset value prediction across different fields, this paper study aims to harness the power of Artificial Intelligence (AI) in forecasting asset prices and tailoring investment strategies. Implemented system integrates the Prophet Model for precise price forecasting and employs Genetic Algorithms for investment strategy generation. Through a systematic evaluation of the system, we demonstrate its capacity to provide accurate asset price predictions, outperform traditional investment strategies and mitigate risks effectively. Empirical unit testing showcased impressive results such as gold model with a 4.76% MAPE and an R-squared value of 0.9795 and oil model with notable metrics such as a Mean Absolute Error of 6.80, and Root Mean Squared Error of 10.92. Every single user, across the board, either strongly agreed or agreed that the investment recommendations provided valuable insights and 92.4% perceiving system predictions as very accurate. It further delves into the challenges and limitations, such as the quality of data used and model interpretability, underscoring the imperative for robust, compliant and interpretable forecasting models. Additionally, the study explores future directions in the domain, advocating for the expansion of asset classes and the integration of Natural Language Processing (NLP) into the system
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