Update README.md

README.md

@@ -8,12 +8,64 @@ This repository contains models for forecasting Apple stock prices using ARIMA a
 
 ## Inference Instructions
 
-To run inference, navigate to the specific model folder. Each folder contains the necessary notebook and model files for making predictions.
+You can either navigate to the specific model folder and open the provided notebook, or run the inference code directly below.
 
-- **ARIMA Model**:  
-  Folder: `Apple-Stock-Price-Forecasting-ARIMA-Model`  
-  - Open the provided inference notebook to run predictions.
+---
+
+<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})
 
-- **LSTM Model**:  
-  Folder: `Apple-Stock-Price-Forecasting-LSTM-Model`  
-  - Open the inference notebook and ensure the pre-trained model file is in the same folder before running.
+print("\nARIMA - 7-Day Forecast")
+print("="*60)
+print(arima_results_df.to_string(index=False))