Add forecasting service

services/forecasting/app/models/predictions.py

@@ -0,0 +1,67 @@
+# ================================================================
+# services/forecasting/app/models/predictions.py
+# ================================================================
+"""
+Additional prediction models for the forecasting service
+"""
+
+from sqlalchemy import Column, String, Integer, Float, DateTime, Boolean, Text, JSON
+from sqlalchemy.dialects.postgresql import UUID
+from datetime import datetime, timezone
+import uuid
+
+from shared.database.base import Base
+
+class ModelPerformanceMetric(Base):
+    """Track model performance over time"""
+    __tablename__ = "model_performance_metrics"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    model_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    product_name = Column(String(255), nullable=False)
+    
+    # Performance metrics
+    mae = Column(Float)  # Mean Absolute Error
+    mape = Column(Float)  # Mean Absolute Percentage Error
+    rmse = Column(Float)  # Root Mean Square Error
+    accuracy_score = Column(Float)
+    
+    # Evaluation period
+    evaluation_date = Column(DateTime(timezone=True), nullable=False)
+    evaluation_period_start = Column(DateTime(timezone=True))
+    evaluation_period_end = Column(DateTime(timezone=True))
+    
+    # Metadata
+    sample_size = Column(Integer)
+    created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
+    
+    def __repr__(self):
+        return f"<ModelPerformanceMetric(model_id={self.model_id}, mae={self.mae})>"
+
+class PredictionCache(Base):
+    """Cache frequently requested predictions"""
+    __tablename__ = "prediction_cache"
+    
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    cache_key = Column(String(255), unique=True, nullable=False, index=True)
+    
+    # Cached data
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+    product_name = Column(String(255), nullable=False)
+    location = Column(String(255), nullable=False)
+    forecast_date = Column(DateTime(timezone=True), nullable=False)
+    
+    # Cached results
+    predicted_demand = Column(Float, nullable=False)
+    confidence_lower = Column(Float, nullable=False)
+    confidence_upper = Column(Float, nullable=False)
+    model_id = Column(UUID(as_uuid=True), nullable=False)
+    
+    # Cache metadata
+    created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
+    expires_at = Column(DateTime(timezone=True), nullable=False)
+    hit_count = Column(Integer, default=0)
+    
+    def __repr__(self):
+        return f"<PredictionCache(key={self.cache_key}, product={self.product_name})>"