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| license: mit |
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| # Apple Stock Price Forecasting |
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| This repository contains models for forecasting Apple stock prices using ARIMA and LSTM. |
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| ## Overview |
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| This project provides pre-trained models for predicting Apple (AAPL) stock prices: |
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| - **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. |
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| Both models use the last 3 months of stock data to generate a 7-day forecast. |
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| ## Inference Instructions |
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| You can perform inference in one of two ways: |
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| 1. **Run the provided inference notebooks** |
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| Each model folder contains a ready-to-run notebook along with the pre-trained model files: |
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| - **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) |
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| - **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) |
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| 2. **Use the code from the notebooks directly in your Python environment** |
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| 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 |
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| > 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. |
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| ## License |
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| This project is licensed under the MIT License. |
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