| tags: | |
| - time-series | |
| - regression | |
| - svr | |
| - stock-prediction | |
| - technical-analysis | |
| - scikit-learn | |
| # SVR Model for AAPL Price Prediction (Technical Indicators) | |
| This repository hosts a trained **Support Vector Regression (SVR)** model and its necessary preprocessing components (StandardScaler) for predicting the closing price of **AAPL**. | |
| ## Model Details | |
| - **Algorithm:** Support Vector Regression (SVR) with RBF/Linear Kernel (Tuned by Grid Search) | |
| - **Features:** 37 features derived from technical analysis (SMA, Volatility, Returns) with lookbacks up to 252 days. | |
| - **Target:** Next day's closing price. | |
| - **Training Period:** 2023-01-01 to 2024-12-31 | |
| ## Inference | |
| To use this model, you must correctly calculate and input all 37 technical features (including moving averages and volatility ratios) for the day prior to the prediction. | |
| 1. Load the `svr_model.joblib` and `standard_scaler.joblib`. | |
| 2. Calculate the 37 features for day $T$. | |
| 3. Scale the 37 features using the loaded `StandardScaler`. | |
| 4. Run the prediction. | |