ETS Forecaster

ads/model/model_metadata.py

@@ -165,6 +165,8 @@ class Framework(ExtendedEnum):
     PYOD = "pyod"
     SPACY = "spacy"
     PROPHET = "prophet"
+    THETA = "theta"
+    ETSForecaster = "ets"
     SKTIME = "sktime"
     STATSMODELS = "statsmodels"
     CUML = "cuml"

ads/opctl/operator/lowcode/common/utils.py

@@ -23,6 +23,7 @@
     InvalidParameterError,
 )
 from ads.secrets import ADBSecretKeeper
+from sktime.param_est.seasonality import SeasonalityACF
 
 
 def call_pandas_fsspec(pd_fn, filename, storage_options, **kwargs):
@@ -385,3 +386,63 @@ def enable_print():
     except Exception:
         pass
     sys.stdout = sys.__stdout__
+
+
+def find_seasonal_period_from_dataset(data: pd.DataFrame) -> tuple[int, list]:
+    try:
+        sp_est = SeasonalityACF()
+        sp_est.fit(data)
+        sp = sp_est.get_fitted_params()["sp"]
+        probable_sps = sp_est.get_fitted_params()["sp_significant"]
+        return sp, probable_sps
+    except Exception as e:
+        logger.warning(f"Unable to find seasonal period: {e}")
+        return None, None
+
+
+def normalize_frequency(freq: str) -> str:
+    """
+    Normalize pandas frequency strings to sktime/period-compatible formats.
+
+    Args:
+        freq: Pandas frequency string
+
+    Returns:
+        Normalized frequency string compatible with PeriodIndex
+    """
+    if freq is None:
+        return None
+
+    freq = freq.upper()
+
+    # Handle weekly frequencies with day anchors (W-SUN, W-MON, etc.)
+    if freq.startswith("W-"):
+        return "W"
+
+    # Handle month start/end frequencies
+    freq_mapping = {
+        "MS": "M",  # Month Start -> Month End
+        "ME": "M",  # Month End -> Month
+        "BMS": "M",  # Business Month Start -> Month
+        "BME": "M",  # Business Month End -> Month
+        "QS": "Q",  # Quarter Start -> Quarter
+        "QE": "Q",  # Quarter End -> Quarter
+        "BQS": "Q",  # Business Quarter Start -> Quarter
+        "BQE": "Q",  # Business Quarter End -> Quarter
+        "YS": "Y",  # Year Start -> Year (Alias: A)
+        "AS": "Y",  # Year Start -> Year (Alias: A)
+        "YE": "Y",  # Year End -> Year
+        "AE": "Y",  # Year End -> Year
+        "BYS": "Y",  # Business Year Start -> Year
+        "BAS": "Y",  # Business Year Start -> Year
+        "BYE": "Y",  # Business Year End -> Year
+        "BAE": "Y",  # Business Year End -> Year
+    }
+
+    # Handle frequencies with prefixes (e.g., "2W", "3M")
+    for old_freq, new_freq in freq_mapping.items():
+        if freq.endswith(old_freq):
+            prefix = freq[:-len(old_freq)]
+            return f"{prefix}{new_freq}" if prefix else new_freq
+
+    return freq

ads/opctl/operator/lowcode/forecast/const.py

@@ -15,6 +15,8 @@ class SupportedModels(ExtendedEnum):
     NeuralProphet = "neuralprophet"
     LGBForecast = "lgbforecast"
     AutoMLX = "automlx"
+    Theta = "theta"
+    ETSForecaster = "ets"
     AutoTS = "autots"
     # Auto = "auto"
 

ads/opctl/operator/lowcode/forecast/model/base_model.py

@@ -962,3 +962,38 @@ def _get_unique_filename(self, base_path: str, storage_options: dict = None) ->
                     f"Error checking OCI path existence: {e}. Using original path."
                 )
                 return base_path
+
+    def generate_explanation_report_from_data(self) -> tuple[rc.Block, rc.Block]:
+        if not self.target_cat_col:
+            self.formatted_global_explanation = (
+                self.formatted_global_explanation.rename(
+                    {"Series 1": self.original_target_column},
+                    axis=1,
+                )
+            )
+            self.formatted_local_explanation.drop(
+                "Series", axis=1, inplace=True
+            )
+
+        # Create a markdown section for the global explainability
+        global_explanation_section = rc.Block(
+            rc.Heading("Global Explainability", level=2),
+            rc.Text(
+                "The following tables provide the feature attribution for the global explainability."
+            ),
+            rc.DataTable(self.formatted_global_explanation, index=True),
+        )
+
+        blocks = [
+            rc.DataTable(
+                local_ex_df.drop("Series", axis=1),
+                label=s_id if self.target_cat_col else None,
+                index=True,
+            )
+            for s_id, local_ex_df in self.local_explanation.items()
+        ]
+        local_explanation_section = rc.Block(
+            rc.Heading("Local Explanation of Models", level=2),
+            rc.Select(blocks=blocks) if len(blocks) > 1 else blocks[0],
+        )
+        return global_explanation_section, local_explanation_section