fix frequency inference

services/inference/tsfminference/inference.py

@@ -115,6 +115,8 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO
             # train to estimate freq if not available
             preprocessor.train(data)
 
+        LOGGER.info(f"Data frequency determined: {preprocessor.freq}")
+
         # warn if future data is not provided, but is needed by the model
         if preprocessor.exogenous_channel_indices and future_data is None:
             raise ValueError(

tsfm_public/toolkit/time_series_forecasting_pipeline.py

@@ -3,7 +3,7 @@
 """Hugging Face Pipeline for Time Series Tasks"""
 
 import inspect
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 
 import numpy as np
 import pandas as pd
@@ -116,13 +116,11 @@ def __init__(
         self,
         model: Union["PreTrainedModel"],
         *args,
-        freq: Optional[str] = None,
         explode_forecasts: bool = False,
         inverse_scale_outputs: bool = True,
         add_known_ground_truth: bool = True,
         **kwargs,
     ):
-        kwargs["freq"] = freq
         kwargs["explode_forecasts"] = explode_forecasts
         kwargs["inverse_scale_outputs"] = inverse_scale_outputs
         kwargs["add_known_ground_truth"] = add_known_ground_truth
@@ -142,10 +140,6 @@ def __init__(
                 if p not in kwargs:
                     kwargs[p] = getattr(kwargs["feature_extractor"], p)
 
-            # get freq from kwargs or the preprocessor
-            if "freq" not in kwargs:
-                kwargs["freq"] = kwargs["feature_extractor"].freq
-
         if "context_length" not in kwargs:
             kwargs["context_length"] = model.config.context_length