Update README.md

README.md

@@ -6,129 +6,42 @@ license: mit
 
 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 either navigate to the specific model folder and open the provided notebook, or run the inference code directly below.
+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
 
-<details>
-<summary>ARIMA Model Inference</summary>
-
-```python
-# Install required packages
-!pip install --quiet yfinance joblib pmdarima huggingface_hub
-
-# Import Libraries
-from huggingface_hub import hf_hub_download
-import joblib
-import numpy as np
-import pandas as pd
-import yfinance as yf
-
-HF_TOKEN = "your_own_hf_token"
-
-# Load ARIMA model and Box-Cox transformer
-arima_model_path = hf_hub_download(
-    repo_id="EsferSami/DataSynthis_ML_JobTask",
-    filename="Apple-Stock-Price-Forecasting-ARIMA-Model/apple_stock_arima.pkl",
-    token=HF_TOKEN
-)
-bct_path = hf_hub_download(
-    repo_id="EsferSami/DataSynthis_ML_JobTask",
-    filename="Apple-Stock-Price-Forecasting-ARIMA-Model/boxcox_transformer.pkl",
-    token=HF_TOKEN
-)
-
-arima_model = joblib.load(arima_model_path)
-bct = joblib.load(bct_path)
-
-# Download recent data
-data = yf.download("AAPL", period="3mo", auto_adjust=False)
-recent_prices = data['Adj Close'].values.astype(float)
-
-# Transform and forecast
-y_trans, _ = bct.transform(recent_prices)
-resid_std = np.std(arima_model.resid()) if hasattr(arima_model, "resid") else np.std(y_trans - np.mean(y_trans))
-
-predictions_trans = []
-current_series = y_trans.copy()
-for day in range(7):
-    try:
-        pred = arima_model.predict(n_periods=1)[0]
-    except Exception:
-        pred = current_series[-1]
-    pred = current_series[-1] + np.random.normal(0.0, resid_std*0.3)
-    predictions_trans.append(pred)
-    current_series = np.append(current_series, pred)
-
-predictions_price, _ = bct.inverse_transform(np.array(predictions_trans))
-prediction_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=7)
-arima_results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': predictions_price})
-
-print("\nARIMA - 7-Day Forecast")
-print("="*60)
-print(arima_results_df.to_string(index=False))
-
-# Install required packages
-!pip install --quiet yfinance joblib tensorflow huggingface_hub scikit-learn
-
-# Import Libraries
-from huggingface_hub import hf_hub_download
-import tensorflow as tf
-import joblib
-import numpy as np
-import pandas as pd
-import yfinance as yf
-from sklearn.preprocessing import MinMaxScaler
-
-HF_TOKEN = "your_own_hf_token"
-
-# Load model and scaler
-model_path = hf_hub_download(
-    repo_id="EsferSami/DataSynthis_ML_JobTask",
-    filename="Apple-Stock-Price-Forecasting-LSTM-Model/apple_stock_lstm.h5",
-    token=HF_TOKEN
-)
-scaler_path = hf_hub_download(
-    repo_id="EsferSami/DataSynthis_ML_JobTask",
-    filename="Apple-Stock-Price-Forecasting-LSTM-Model/scaler.joblib",
-    token=HF_TOKEN
-)
-
-model = tf.keras.models.load_model(model_path)
-scaler = joblib.load(scaler_path)
-
-# Download recent data
-data = yf.download("AAPL", period="3mo", auto_adjust=False)
-recent_prices = data['Adj Close'].values.astype(float)
-
-# Prepare input
-last_60_days = recent_prices[-60:].reshape(-1, 1)
-last_60_scaled = scaler.transform(last_60_days)
-
-predictions = []
-current_seq = last_60_scaled.copy()
-last_price = last_60_days[-1][0]
-MAX_DAILY_CHANGE = 0.02
-
-for day in range(7):
-    input_data = current_seq.reshape(1, 60, 1)
-    pred_scaled = model.predict(input_data, verbose=0)
-    pred_price_raw = scaler.inverse_transform(pred_scaled)[0][0]
-    
-    change = pred_price_raw - last_price
-    change = np.clip(change, -MAX_DAILY_CHANGE*last_price, MAX_DAILY_CHANGE*last_price)
-    anchored_price = last_price + change
-    predictions.append(anchored_price)
-    
-    pred_scaled_reshaped = scaler.transform(np.array([[anchored_price]]))
-    current_seq = np.append(current_seq[1:], pred_scaled_reshaped, axis=0)
-    last_price = anchored_price
-
-prediction_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=7)
-results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': np.round(predictions, 2)})
-
-print("\nLSTM - 7-Day Forecast")
-print("="*50)
-print(results_df.to_string(index=False))
+This project is licensed under the MIT License.