Add improvements

services/training/app/ml/model_selector.py

@@ -142,10 +142,25 @@ class ModelSelector:
         # Zero ratio
         zero_ratio = (y == 0).sum() / len(y)
 
-        # Seasonality strength (simple proxy using rolling std)
+        # Seasonality strength using autocorrelation at key lags (7 days, 30 days)
+        # This better captures periodic patterns without using future data
         if len(df) >= 14:
-            rolling_mean = pd.Series(y).rolling(window=7, center=True).mean()
-            seasonality_strength = rolling_mean.std() / (np.std(y) + 1e-6) if np.std(y) > 0 else 0
+            # Calculate autocorrelation at weekly lag (7 days)
+            # Higher autocorrelation indicates stronger weekly patterns
+            try:
+                weekly_autocorr = pd.Series(y).autocorr(lag=7) if len(y) > 7 else 0
+
+                # Calculate autocorrelation at monthly lag if enough data
+                monthly_autocorr = pd.Series(y).autocorr(lag=30) if len(y) > 30 else 0
+
+                # Combine autocorrelations (weekly weighted more for bakery data)
+                seasonality_strength = abs(weekly_autocorr) * 0.7 + abs(monthly_autocorr) * 0.3
+
+                # Ensure in valid range [0, 1]
+                seasonality_strength = max(0.0, min(1.0, seasonality_strength))
+            except Exception:
+                # Fallback to simpler calculation if autocorrelation fails
+                seasonality_strength = 0.5
         else:
             seasonality_strength = 0.5  # Default