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modeling_timellm.py

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+"""
+TimeLLM Model for Supply Chain Demand Forecasting
+Based on the TimeLLM framework: https://github.com/KimMeen/Time-LLM
+"""
+
+import torch
+import json
+import numpy as np
+from typing import Dict, List, Any
+
+class TimeLLMForecaster:
+    """
+    TimeLLM model for supply chain demand forecasting.
+    
+    This model was trained on AWS SageMaker using the TimeLLM framework
+    to forecast demand patterns in supply chain data.
+    """
+    
+    def __init__(self, model_path: str = "model.pth", config_path: str = "config.json"):
+        """
+        Initialize the TimeLLM forecaster.
+        
+        Args:
+            model_path: Path to the trained model weights
+            config_path: Path to the model configuration
+        """
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Load configuration
+        with open(config_path, 'r') as f:
+            self.config = json.load(f)
+        
+        # Load model weights
+        self.model_state = torch.load(model_path, map_location=self.device)
+        
+        print(f"TimeLLM model loaded successfully on {self.device}")
+        print(f"Model configuration: {self.config['model_name']}")
+        print(f"Sequence length: {self.config['seq_len']}")
+        print(f"Prediction length: {self.config['pred_len']}")
+        print(f"Features: {self.config['enc_in']}")
+    
+    def forecast(self, 
+                 historical_data: np.ndarray, 
+                 time_features: np.ndarray) -> np.ndarray:
+        """
+        Generate demand forecasts.
+        
+        Args:
+            historical_data: Historical time series data (seq_len, n_features)
+            time_features: Time-based features (seq_len, time_features)
+            
+        Returns:
+            Forecasted values (pred_len, n_features)
+        """
+        # This is a placeholder - actual inference would require
+        # the full TimeLLM model implementation
+        print("Forecasting with TimeLLM...")
+        print(f"Input shape: {historical_data.shape}")
+        print(f"Time features shape: {time_features.shape}")
+        
+        # Return dummy forecast for demonstration
+        pred_len = self.config['pred_len']
+        n_features = self.config['c_out']
+        
+        return np.random.randn(pred_len, n_features)
+    
+    def get_model_info(self) -> Dict[str, Any]:
+        """Get model information and training details."""
+        return {
+            "model_name": self.config['model_name'],
+            "base_model": self.config['base_model'],
+            "training_platform": self.config['trained_on'],
+            "training_job": self.config['training_job'],
+            "training_time": self.config['training_time'],
+            "instance_type": self.config['instance_type'],
+            "seq_len": self.config['seq_len'],
+            "pred_len": self.config['pred_len'],
+            "features": self.config['enc_in']
+        }
+
+# Example usage
+if __name__ == "__main__":
+    # Initialize the forecaster
+    forecaster = TimeLLMForecaster()
+    
+    # Print model information
+    info = forecaster.get_model_info()
+    print("\nModel Information:")
+    for key, value in info.items():
+        print(f"  {key}: {value}")
+    
+    # Example forecast (with dummy data)
+    seq_len = 96
+    n_features = 14
+    time_features = 3
+    
+    historical_data = np.random.randn(seq_len, n_features)
+    time_features_data = np.random.randn(seq_len, time_features)
+    
+    forecast = forecaster.forecast(historical_data, time_features_data)
+    print(f"\nForecast shape: {forecast.shape}")