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: 2010-01-01 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.
- Load the
svr_model.joblib and standard_scaler.joblib.
- Calculate the 37 features for day $T$.
- Scale the 37 features using the loaded
StandardScaler.
- Run the prediction.
