return Naive model for constant series in AutoCES (#821)

nbs/src/core/models.ipynb

@@ -78,8 +78,11 @@
     "\n",
     "from statsforecast.arima import (\n",
     "    Arima,\n",
-    "    auto_arima_f, forecast_arima, \n",
-    "    fitted_arima, forward_arima\n",
+    "    auto_arima_f,\n",
+    "    fitted_arima,\n",
+    "    forecast_arima, \n",
+    "    forward_arima,\n",
+    "    is_constant,\n",
     ")\n",
     "from statsforecast.ces import (\n",
     "    auto_ces, forecast_ces,\n",
@@ -1680,8 +1683,12 @@
     "        self : \n",
     "            Complex Exponential Smoothing fitted model.\n",
     "        \"\"\"\n",
+    "        if is_constant(y):\n",
+    "            model = Naive(alias=self.alias, prediction_intervals=self.prediction_intervals)\n",
+    "            model.fit(y=y, X=X)\n",
+    "            return model\n",
     "        self.model_ = auto_ces(y, m=self.season_length, model=self.model)\n",
-    "        self.model_['actual_residuals'] = y - self.model_['fitted']\n",
+    "        self.model_['actual_residuals'] = y - self.model_['fitted']        \n",
     "        self._store_cs(y=y, X=X)\n",
     "        return self\n",
     "    \n",
@@ -1777,6 +1784,9 @@
     "        forecasts : dict \n",
     "            Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n",
     "        \"\"\"\n",
+    "        if is_constant(y):\n",
+    "            model = Naive(alias=self.alias, prediction_intervals=self.prediction_intervals)\n",
+    "            return model.forecast(y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted)\n",
     "        mod = auto_ces(y, m=self.season_length, model=self.model)\n",
     "        fcst = forecast_ces(mod, h, level=level)\n",
     "        keys = ['mean']\n",

statsforecast/models.py

@@ -22,9 +22,10 @@
 from statsforecast.arima import (
     Arima,
     auto_arima_f,
-    forecast_arima,
     fitted_arima,
+    forecast_arima,
     forward_arima,
+    is_constant,
 )
 from .ces import auto_ces, forecast_ces, forward_ces
 from statsforecast.ets import (
@@ -962,6 +963,12 @@ def fit(
         self :
             Complex Exponential Smoothing fitted model.
         """
+        if is_constant(y):
+            model = Naive(
+                alias=self.alias, prediction_intervals=self.prediction_intervals
+            )
+            model.fit(y=y, X=X)
+            return model
         self.model_ = auto_ces(y, m=self.season_length, model=self.model)
         self.model_["actual_residuals"] = y - self.model_["fitted"]
         self._store_cs(y=y, X=X)
@@ -1056,6 +1063,13 @@ def forecast(
         forecasts : dict
             Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.
         """
+        if is_constant(y):
+            model = Naive(
+                alias=self.alias, prediction_intervals=self.prediction_intervals
+            )
+            return model.forecast(
+                y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted
+            )
         mod = auto_ces(y, m=self.season_length, model=self.model)
         fcst = forecast_ces(mod, h, level=level)
         keys = ["mean"]
@@ -2375,6 +2389,7 @@ def _seasonal_ses_optimized(
 
 # %% ../nbs/src/core/models.ipynb 161
 class SeasonalExponentialSmoothingOptimized(_TS):
+
     def __init__(
         self,
         season_length: int,
@@ -2579,6 +2594,7 @@ def __init__(
         alias: str = "Holt",
         prediction_intervals: Optional[ConformalIntervals] = None,
     ):
+
         self.season_length = season_length
         self.error_type = error_type
         self.alias = alias
@@ -2648,6 +2664,7 @@ def _historic_average(
 
 # %% ../nbs/src/core/models.ipynb 204
 class HistoricAverage(_TS):
+
     def __init__(
         self,
         alias: str = "HistoricAverage",
@@ -2826,6 +2843,7 @@ def forecast(
 
 # %% ../nbs/src/core/models.ipynb 217
 class Naive(_TS):
+
     def __init__(
         self,
         alias: str = "Naive",
@@ -3054,6 +3072,7 @@ def _random_walk_with_drift(
 
 # %% ../nbs/src/core/models.ipynb 234
 class RandomWalkWithDrift(_TS):
+
     def __init__(
         self,
         alias: str = "RWD",
@@ -3232,6 +3251,7 @@ def forecast(
 
 # %% ../nbs/src/core/models.ipynb 249
 class SeasonalNaive(_TS):
+
     def __init__(
         self,
         season_length: int,
@@ -3435,6 +3455,7 @@ def _window_average(
 
 # %% ../nbs/src/core/models.ipynb 265
 class WindowAverage(_TS):
+
     def __init__(
         self,
         window_size: int,
@@ -3610,6 +3631,7 @@ def _seasonal_window_average(
 
 # %% ../nbs/src/core/models.ipynb 277
 class SeasonalWindowAverage(_TS):
+
     def __init__(
         self,
         season_length: int,
@@ -3865,6 +3887,7 @@ def _adida(
 
 # %% ../nbs/src/core/models.ipynb 290
 class ADIDA(_TS):
+
     def __init__(
         self,
         alias: str = "ADIDA",
@@ -4062,6 +4085,7 @@ def _croston_classic(
 
 # %% ../nbs/src/core/models.ipynb 303
 class CrostonClassic(_TS):
+
     def __init__(
         self,
         alias: str = "CrostonClassic",
@@ -4268,6 +4292,7 @@ def _croston_optimized(
 
 # %% ../nbs/src/core/models.ipynb 315
 class CrostonOptimized(_TS):
+
     def __init__(
         self,
         alias: str = "CrostonOptimized",
@@ -4441,6 +4466,7 @@ def _croston_sba(
 
 # %% ../nbs/src/core/models.ipynb 327
 class CrostonSBA(_TS):
+
     def __init__(
         self,
         alias: str = "CrostonSBA",
@@ -4641,6 +4667,7 @@ def _imapa(
 
 # %% ../nbs/src/core/models.ipynb 339
 class IMAPA(_TS):
+
     def __init__(
         self,
         alias: str = "IMAPA",
@@ -4831,6 +4858,7 @@ def _tsb(
 
 # %% ../nbs/src/core/models.ipynb 351
 class TSB(_TS):
+
     def __init__(
         self,
         alpha_d: float,
@@ -5058,6 +5086,7 @@ def __init__(
         alias: str = "MSTL",
         prediction_intervals: Optional[ConformalIntervals] = None,
     ):
+
         # check ETS model doesnt have seasonality
         if repr(trend_forecaster) == "AutoETS":
             if trend_forecaster.model[2] != "N":
@@ -6177,6 +6206,7 @@ def forward(
 
 # %% ../nbs/src/core/models.ipynb 490
 class ConstantModel(_TS):
+
     def __init__(self, constant: float, alias: str = "ConstantModel"):
         """Constant Model.
 
@@ -6362,6 +6392,7 @@ def forward(
 
 # %% ../nbs/src/core/models.ipynb 504
 class ZeroModel(ConstantModel):
+
     def __init__(self, alias: str = "ZeroModel"):
         """Returns Zero forecasts.
 
@@ -6376,6 +6407,7 @@ def __init__(self, alias: str = "ZeroModel"):
 
 # %% ../nbs/src/core/models.ipynb 518
 class NaNModel(ConstantModel):
+
     def __init__(self, alias: str = "NaNModel"):
         """NaN Model.