added darts.NaiveMovingAverage

models/dt-naive-moving-avg/Dockerfile

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+FROM attilabalint/enfobench-models:base-u8darts-0.27.2
+
+WORKDIR /usr/local/app
+
+COPY ./requirements.txt /usr/local/app/requirements.txt
+RUN pip install --no-cache-dir -r /usr/local/app/requirements.txt
+
+COPY ./src /usr/local/app/src
+
+ENV ENFOBENCH_MODEL_HISTORY="1D"
+
+EXPOSE 3000
+CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "3000"]

models/dt-naive-moving-avg/requirements.txt

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+enfobench>=0.6.0,<0.7.0

models/dt-naive-moving-avg/src/main.py

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+import os
+
+import pandas as pd
+from darts import TimeSeries
+from darts.models.forecasting.baselines import NaiveMovingAverage
+
+from enfobench import AuthorInfo, ForecasterType, ModelInfo
+from enfobench.evaluation.server import server_factory
+from enfobench.evaluation.utils import periods_in_duration
+
+
+class DartsNaiveMovingAverageModel:
+    def __init__(self, history: str):
+        self.history = history.upper()
+
+    def info(self) -> ModelInfo:
+        return ModelInfo(
+            name=f"Darts.NaiveMovingAverage.{self.history}",
+            authors=[AuthorInfo(name="Attila Balint", email="[email protected]")],
+            type=ForecasterType.point,
+            params={
+                "history": self.history,
+            },
+        )
+
+    def forecast(
+        self,
+        horizon: int,
+        history: pd.DataFrame,
+        past_covariates: pd.DataFrame | None = None,
+        future_covariates: pd.DataFrame | None = None,
+        metadata: dict | None = None,
+        **kwargs,
+    ) -> pd.DataFrame:
+        # Fill missing values
+        history = history.fillna(history.y.mean())
+
+        # Create model
+        periods = periods_in_duration(history.index, duration=self.history)
+        model = NaiveMovingAverage(input_chunk_length=periods)
+
+        # Fit model
+        series = TimeSeries.from_dataframe(history, value_cols=["y"])
+        model.fit(series)
+
+        # Make forecast
+        pred = model.predict(horizon)
+
+        # Postprocess forecast
+        forecast = pred.pd_dataframe().rename(columns={"y": "yhat"}).fillna(history.y.mean())
+        return forecast
+
+
+# Load parameters
+history = os.getenv("ENFOBENCH_MODEL_HISTORY")
+
+# Instantiate your model
+model = DartsNaiveMovingAverageModel(history)
+
+# Create a forecast server by passing in your model
+app = server_factory(model)