Stock Prediction using Deep learning

Abstract

Stock market prediction has long been a subject of interest for investors, analysts, and researchers due to its potential to yield high financial returns. However, the volatile and non-linear nature of financial markets makes forecasting a challenging task accurate. This project explores the use of the ARIMA (Auto Regressive Integrated Moving Average) model for predicting stock prices, leveraging its strength in modeling time series data with trends and seasonality.The study involves the collection and preprocessing of historical stock data, followed by stationary testing, model selection using AIC/BIC criteria, and parameter tuning through grid search. The final ARIMA model is evaluated using standard performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).The results demonstrate that ARIMA can effectively capture short-term trends in stock prices, though it may be limited in handling abrupt market shifts or external factors influencing the market.Overall, this project provides insights into the applicability of statistical time series models like ARIMA in financial forecasting and highlights both their strengths and limitations in the context of stock market prediction.

Introduction

The stock market is a vital component of the global financial system, facilitating the trading of equity shares and influencing investment and economic growth. However, stock price prediction is complex due to market volatility and various external factors. Traditional analysis includes fundamental and technical approaches, while recent advances leverage data science and machine learning, particularly time series models like ARIMA.

The ARIMA model, which uses autoregression, differencing, and moving averages, is effective for univariate stock price forecasting due to its statistical rigor and interpretability. This paper proposes using ARIMA for stock price prediction through data preprocessing, parameter identification, model training, and evaluation using metrics like MAE and RMSE.

The literature survey highlights the evolution of stock market prediction techniques, from traditional statistical methods to deep learning, graph-based models, multimodal learning, and reinforcement learning, reflecting a trend toward integrating diverse data types and advanced neural architectures for better accuracy.

The proposed system implements machine learning models (notably LSTM outperforming SVM and backpropagation networks) for predicting stock trends using historical data. The ARIMA algorithm is detailed step-by-step, including data collection, stationarity checks, parameter selection, modeling, forecasting, and evaluation. The mathematical formulation of ARIMA’s components (AR, I, MA) is explained along with model fitting procedures.

Overall, the study presents a structured and modular framework combining statistical methods and AI techniques to enhance stock market forecasting accuracy, aiding investors and firms in decision-making and improving financial transparency.

Conclusion

In this manner, as we can see above in our proposed strategy, we train the information utilizing existing stock dataset that is accessible. We utilize this information to foresee and gauge the stock cost of n-days into what\'s in store. The typical presentation of the model abatements with expansion in number of days, because of eccentric changes in pattern. The ongoing framework can refresh its preparation set as every day passes in order to identify fresher patterns and act like a web based learning framework that predicts stock continuously. In this undertaking, the proposed project addresses a critical step in propelling the precision and strength of financial exchange pattern expectations by synergistically coordinating the Autoregressive Coordinated Moving Normal (ARIMA) calculation with AI models. The undertaking started with careful information assortment, including different verifiable stock information to guarantee the model\'s versatility. Through thorough preprocessing and highlight designing, the dataset was refined to upgrade its quality and significance. The combination of ARIMA with AI models intended to catch both worldly conditions and extra perplexing highlights, giving a comprehensive comprehension of the variables impacting stock patterns. All through the task, the mixture model went through fastidious preparation, enhancement, and assessment, showing its capacities across different datasets and economic situations. Near examinations with benchmark models highlighted the upsides of the incorporated methodology, featuring worked on prescient exactness and speculation capacities. The model\'s interpretability was upgraded, giving important bits of knowledge into the elements driving forecasts, hence supporting partners in settling on informed choices. The meaning of this work lies in its capability to offer more exact and dependable expectations for securities exchange patterns, pivotal for financial backers, monetary examiners, and policymakers. The venture additionally recognizes its constraints, like the intrinsic vulnerabilities in monetary business sectors and the powerful idea of financial variables. As a future heading, progressing refinement of the model, investigation of extra highlights, and variation to arising monetary instruments will be central. Further exploration can likewise dive into ongoing execution and contemplations for reasonable sending in monetary dynamic cycles. Generally, the proposed project lays the foundation for a coordinated methodology that spans the qualities of customary time series examination and current AI, adding to the continuous development of prescient investigation in the unique scene of monetary business sectors.

References

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Copyright

Copyright © 2025 Anisha Raja. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.