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@article{196001,
        author = {Madhumitha.R and Vijitha reddy.M and Keerthikasri.R},
        title = {APPLE STOCK PRICE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12181-12188},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196001},
        abstract = {Stock market prediction has become an important research area in financial analytics due to its potential to support investment decisions and reduce financial risk. This paper presents a comparative study of statistical, machine learning, and deep learning models for predicting the stock price of Apple Inc. (AAPL). Historical stock market data containing daily trading information such as opening price, closing price, highest price, lowest price, adjusted closing price, and trading volume is analyzed. Exploratory Data Analysis (EDA) is performed to identify trends, volatility, and distribution patterns within the dataset. Classical time-series models such as AutoRegressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) are implemented as baseline forecasting models. In addition, machine learning models including Random Forest and XGBoost are applied to capture nonlinear relationships between features. A deep learning model based on Long Short-Term Memory (LSTM) networks is also implemented to learn temporal dependencies in the time series. Model performance is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Experimental results show that the LSTM model achieves the highest prediction accuracy. Finally, the best-performing model is used to forecast Apple stock prices for the next 30 trading days. The results demonstrate that deep learning approaches provide more reliable predictions for financial time-series data.},
        keywords = {},
        month = {April},
        }