README.md

metadata

tags:
  - time-series
  - regression
  - svr
  - stock-prediction
  - scikit-learn

SVR Model for AAPL Price Prediction

This repository hosts a trained Support Vector Regression (SVR) model and its necessary preprocessing components (MinMaxScaler) for predicting the closing price of AAPL.

Model Details

• Algorithm: Support Vector Regression (SVR) with RBF Kernel
• Features (Input Sequence Length): 100 days
• Target: Single-step prediction (the price of day $T+1$)
• Training Period: 2020-11-24 to 2025-11-23

Inference

To use this model, you must provide a sequence of the last 100 scaled closing prices.

1. Load the svr_model.joblib and minmax_scaler.joblib.
2. Scale your 100-day input sequence using the loaded MinMaxScaler.
3. Run the prediction.
4. Inverse transform the prediction using the loaded MinMaxScaler to get the final dollar value.