File size: 1,915 Bytes
92ff39e 5733a28 92ff39e 5733a28 92ff39e 5733a28 3e74548 5733a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
license: mit
---
# Apple Stock Price Forecasting
This repository contains models for forecasting Apple stock prices using ARIMA and LSTM.
## Overview
This project provides pre-trained models for predicting Apple (AAPL) stock prices:
- **ARIMA Model** – Classical time series forecasting using ARIMA with Box-Cox transformation.
- **LSTM Model** – Deep learning based forecasting using a trained LSTM network with a scaler.
Both models use the last 3 months of stock data to generate a 7-day forecast.
## Inference Instructions
You can perform inference in one of two ways:
1. **Run the provided inference notebooks**
Each model folder contains a ready-to-run notebook along with the pre-trained model files:
- **ARIMA Model**:
Folder: `Apple-Stock-Price-Forecasting-ARIMA-Model`
Notebook: `inference.ipynb` (includes loading the ARIMA model and Box-Cox transformer, downloading recent AAPL data, and generating a 7-day forecast)
- **LSTM Model**:
Folder: `Apple-Stock-Price-Forecasting-LSTM-Model`
Notebook: `inference.ipynb` (includes loading the LSTM model and scaler, preparing the last 60 days of data, and generating a 7-day forecast)
2. **Use the code from the notebooks directly in your Python environment**
Each notebook contains **fully commented code** showing how to:
- Download recent stock data (`yfinance`)
- Load the pre-trained model from Hugging Face Hub
- Preprocess data (Box-Cox for ARIMA, scaling for LSTM)
- Run 7-day predictions
- Generate a results table with forecasted prices
> Note: If you want to directly run inference without notebooks, you can copy the code from the `inference.ipynb` files in each model folder. The notebooks also include instructions for installing required packages and setting your Hugging Face token.
## License
This project is licensed under the MIT License.
|