ML-008: add statistical baseline model

Closes #19

tests/ml/test_predictor.py

@@ -13,16 +13,26 @@ def _vector(sensor_id: str, temperature: float, label: str | None = None) -> Fea
 
 def predictor() -> Predictor:
     store = FeatureStore()
-    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
+    store.add_batch(
+        [
+            _vector("sensor.kitchen", 19.0),
+            _vector("sensor.kitchen", 20.0),
+            _vector("sensor.bedroom", 18.5),
+        ]
+    )
     pipeline = TrainingPipeline(store)
     pipeline.run("artifact_v1")
     return Predictor(pipeline)
 
 
-def test_predict_returns_expected_format() -> None:
+def test_predict_returns_statistical_forecast() -> None:
     p = predictor()
     result = p.predict("artifact_v1", _vector("sensor.kitchen", 21.0))
-    assert result == "artifact_v1:sensor.kitchen:{'temperature': 21.0}"
+    assert result.artifact_id == "artifact_v1"
+    assert result.sensor_id == "sensor.kitchen"
+    assert result.predictions == {"temperature": 22.0}
+    assert 0.0 < result.confidence <= 1.0
+    assert result.model_type == "statistical_baseline"
 
 
 def test_predict_rejects_unknown_sensor() -> None:
@@ -45,4 +55,4 @@ def test_default_artifact_returns_last_registered() -> None:
     pipeline = TrainingPipeline(store)
     pipeline.run("first")
     pipeline.run("second")
-    assert Predictor.default_artifact(pipeline).artifact_id == "second"
+    assert Predictor.default_artifact(pipeline).artifact_id == "second"