Paper Title
Enhancing Stock Price Prediction with Deep Reinforcement Learning: A Time Series Analysis Approach

In the dynamic domain of financial markets, achieving precise forecasts of stock trends stands as an ongoing challenge. This research paper takes a profound dive into the captivating universe of Deep Reinforcement Learning (DRL) and its transformative role in reshaping stock prediction methodologies, intricately woven into the fabric of time series analysis. The paper involves thorough analysis and readings to explore the patterns and give relatively accurate results with a combined approach that uses traditional Time series techniques with Deep Reinforcement Learning. The DRL model used in the analysis is extremely effective in dealing with the complex data set of stock values and can overall enhance the accuracy of predictions as it is effective in sequential decision making it extremely useful in the constantly changing stock market. The use of DRL significantly reduces the overall dependency on manual feature engineering that is relatively time and resource-consuming by using deep neural networks that learn relevant data directly from the raw time data series.Additionally, the DRL model can effectively adapt to the ever-changing market without many constant manual adjustments thus decreasing the cost and overall maintenance of the feature, and therefore making it more reliable and effective than its counterparts. The results and insights from this research offer valuable contributions to the field of financial forecasting and open avenues for future research to explore even more sophisticated architectures and techniques in pursuit of ever more accurate predictions in dynamic stock markets. Keywords - Data Science; Deep Reinforcement Learning; Machine Learning; Time Series; Deep Learning, Reinforcement learning