**Authors:** Dr. B. Chandrashekar, C. Yosepu, Dr. Saikrishna Boggavarapu, Dr. Sanjeev Chepyala, Sumit Pundir, Dr. G. Manikandan **Published:** _6th International Conference on Contemporary Computing and Informatics (IC3I)_, 2023 **Link:** [IEEE Xplore](https://ieeexplore.ieee.org/) --- ### Summary This study proposes a hybrid financial forecasting model combining #ARIMA and #Gradient-Boosting to improve the accuracy of stock price predictions in the stock exchange market. It leverages ARIMA for time-series analysis and Gradient Boosting for non-linear pattern learning to achieve enhanced forecasting performance. --- ### Key Points - **ARIMA for Time-Series Forecasting:** - Captures #temporal dependencies and long-term #trends. - Effective for both #stationary and #non-stationary financial data. - **Gradient Boosting for Non-Linear Patterns:** - Learns complex relationships missed by traditional statistical models. - Uses residuals from ARIMA as input to refine predictions. - **Hybrid Approach:** - ARIMA is first applied to pre-process the time series and extract residuals. - Gradient Boosting then learns the non-linear features from these residuals. - This combination improves the model’s ability to **predict sudden changes** in stock prices. --- ### How It Works 1. **Data Collection:** Historical financial data including stock prices, trading volumes, and indices. 2. **Data Pre-processing:** Handles missing values, outliers, and normalizes data for ARIMA modeling. 3. **ARIMA Modeling:** Analyzes temporal relationships and generates residuals. 4. **Feature Engineering:** Residuals from ARIMA are treated as new features. 5. **Gradient Boosting Modeling:** Learns non-linear relationships in the data. 6. **Model Training and Evaluation:** Uses Mean Absolute Error #MAE, Mean Squared Error #MSE, and #R² for performance assessment. 7. **Deployment and Monitoring:** Deployed for real-time financial forecasting with ongoing monitoring. --- ### Performance Results - Achieved **97.12% accuracy**, outperforming #LSTM (90.54%), SVM (92%), and Linear Regression (88.12%). - **Mean Absolute Error:** 105.361 (lowest among all compared models) - **Mean Squared Error:** 57.547 (significantly lower than other methods) - **R² Score:** 0.993, indicating very high predictive reliability. --- ### Strengths - Combines both #temporal analysis and #non-linear learning. - Outperforms conventional models in terms of prediction accuracy. - Adapts well to **volatile market conditions** with more precision. --- ### Limitations - Performance may vary with **different market conditions**. - Relies heavily on high-quality historical data for accuracy. - Optimization of hyperparameters is crucial for best results.