🌿 Fern Regeneration -- January 5, 2024

nixtlats/types/__init__.py

@@ -1,15 +1,33 @@
 # This file was auto-generated by Fern from our API Definition.
 
 from .http_validation_error import HttpValidationError
+from .multi_series_anomaly_model import MultiSeriesAnomalyModel
+from .multi_series_cross_validation_finetune_loss import MultiSeriesCrossValidationFinetuneLoss
+from .multi_series_cross_validation_model import MultiSeriesCrossValidationModel
+from .multi_series_forecast_finetune_loss import MultiSeriesForecastFinetuneLoss
+from .multi_series_forecast_model import MultiSeriesForecastModel
 from .multi_series_input import MultiSeriesInput
+from .multi_series_insample_forecast_model import MultiSeriesInsampleForecastModel
 from .single_series_forecast import SingleSeriesForecast
+from .single_series_forecast_finetune_loss import SingleSeriesForecastFinetuneLoss
+from .single_series_forecast_model import SingleSeriesForecastModel
+from .single_series_insample_forecast_model import SingleSeriesInsampleForecastModel
 from .validation_error import ValidationError
 from .validation_error_loc_item import ValidationErrorLocItem
 
 __all__ = [
     "HttpValidationError",
+    "MultiSeriesAnomalyModel",
+    "MultiSeriesCrossValidationFinetuneLoss",
+    "MultiSeriesCrossValidationModel",
+    "MultiSeriesForecastFinetuneLoss",
+    "MultiSeriesForecastModel",
     "MultiSeriesInput",
+    "MultiSeriesInsampleForecastModel",
     "SingleSeriesForecast",
+    "SingleSeriesForecastFinetuneLoss",
+    "SingleSeriesForecastModel",
+    "SingleSeriesInsampleForecastModel",
     "ValidationError",
     "ValidationErrorLocItem",
 ]

nixtlats/types/multi_series_anomaly_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesAnomalyModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is MultiSeriesAnomalyModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is MultiSeriesAnomalyModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()

nixtlats/types/multi_series_cross_validation_finetune_loss.py

@@ -0,0 +1,41 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesCrossValidationFinetuneLoss(str, enum.Enum):
+    """
+    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
+    """
+
+    DEFAULT = "default"
+    MAE = "mae"
+    MSE = "mse"
+    RMSE = "rmse"
+    MAPE = "mape"
+    SMAPE = "smape"
+
+    def visit(
+        self,
+        default: typing.Callable[[], T_Result],
+        mae: typing.Callable[[], T_Result],
+        mse: typing.Callable[[], T_Result],
+        rmse: typing.Callable[[], T_Result],
+        mape: typing.Callable[[], T_Result],
+        smape: typing.Callable[[], T_Result],
+    ) -> T_Result:
+        if self is MultiSeriesCrossValidationFinetuneLoss.DEFAULT:
+            return default()
+        if self is MultiSeriesCrossValidationFinetuneLoss.MAE:
+            return mae()
+        if self is MultiSeriesCrossValidationFinetuneLoss.MSE:
+            return mse()
+        if self is MultiSeriesCrossValidationFinetuneLoss.RMSE:
+            return rmse()
+        if self is MultiSeriesCrossValidationFinetuneLoss.MAPE:
+            return mape()
+        if self is MultiSeriesCrossValidationFinetuneLoss.SMAPE:
+            return smape()

nixtlats/types/multi_series_cross_validation_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesCrossValidationModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is MultiSeriesCrossValidationModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is MultiSeriesCrossValidationModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()

nixtlats/types/multi_series_forecast_finetune_loss.py

@@ -0,0 +1,41 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesForecastFinetuneLoss(str, enum.Enum):
+    """
+    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
+    """
+
+    DEFAULT = "default"
+    MAE = "mae"
+    MSE = "mse"
+    RMSE = "rmse"
+    MAPE = "mape"
+    SMAPE = "smape"
+
+    def visit(
+        self,
+        default: typing.Callable[[], T_Result],
+        mae: typing.Callable[[], T_Result],
+        mse: typing.Callable[[], T_Result],
+        rmse: typing.Callable[[], T_Result],
+        mape: typing.Callable[[], T_Result],
+        smape: typing.Callable[[], T_Result],
+    ) -> T_Result:
+        if self is MultiSeriesForecastFinetuneLoss.DEFAULT:
+            return default()
+        if self is MultiSeriesForecastFinetuneLoss.MAE:
+            return mae()
+        if self is MultiSeriesForecastFinetuneLoss.MSE:
+            return mse()
+        if self is MultiSeriesForecastFinetuneLoss.RMSE:
+            return rmse()
+        if self is MultiSeriesForecastFinetuneLoss.MAPE:
+            return mape()
+        if self is MultiSeriesForecastFinetuneLoss.SMAPE:
+            return smape()

nixtlats/types/multi_series_forecast_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesForecastModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is MultiSeriesForecastModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is MultiSeriesForecastModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()

nixtlats/types/multi_series_insample_forecast_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class MultiSeriesInsampleForecastModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is MultiSeriesInsampleForecastModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is MultiSeriesInsampleForecastModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()

nixtlats/types/single_series_forecast.py

@@ -6,10 +6,12 @@
 import pydantic
 
 from ..core.datetime_utils import serialize_datetime
+from .single_series_forecast_finetune_loss import SingleSeriesForecastFinetuneLoss
+from .single_series_forecast_model import SingleSeriesForecastModel
 
 
 class SingleSeriesForecast(pydantic.BaseModel):
-    model: typing.Optional[str] = pydantic.Field(
+    model: typing.Optional[SingleSeriesForecastModel] = pydantic.Field(
         description="Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data."
     )
     freq: typing.Optional[str] = pydantic.Field(
@@ -31,6 +33,9 @@ class SingleSeriesForecast(pydantic.BaseModel):
     finetune_steps: typing.Optional[int] = pydantic.Field(
         description="The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning."
     )
+    finetune_loss: typing.Optional[SingleSeriesForecastFinetuneLoss] = pydantic.Field(
+        description="The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers."
+    )
 
     def json(self, **kwargs: typing.Any) -> str:
         kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}

nixtlats/types/single_series_forecast_finetune_loss.py

@@ -0,0 +1,41 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class SingleSeriesForecastFinetuneLoss(str, enum.Enum):
+    """
+    The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.
+    """
+
+    DEFAULT = "default"
+    MAE = "mae"
+    MSE = "mse"
+    RMSE = "rmse"
+    MAPE = "mape"
+    SMAPE = "smape"
+
+    def visit(
+        self,
+        default: typing.Callable[[], T_Result],
+        mae: typing.Callable[[], T_Result],
+        mse: typing.Callable[[], T_Result],
+        rmse: typing.Callable[[], T_Result],
+        mape: typing.Callable[[], T_Result],
+        smape: typing.Callable[[], T_Result],
+    ) -> T_Result:
+        if self is SingleSeriesForecastFinetuneLoss.DEFAULT:
+            return default()
+        if self is SingleSeriesForecastFinetuneLoss.MAE:
+            return mae()
+        if self is SingleSeriesForecastFinetuneLoss.MSE:
+            return mse()
+        if self is SingleSeriesForecastFinetuneLoss.RMSE:
+            return rmse()
+        if self is SingleSeriesForecastFinetuneLoss.MAPE:
+            return mape()
+        if self is SingleSeriesForecastFinetuneLoss.SMAPE:
+            return smape()

nixtlats/types/single_series_forecast_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class SingleSeriesForecastModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is SingleSeriesForecastModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is SingleSeriesForecastModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()

nixtlats/types/single_series_insample_forecast_model.py

@@ -0,0 +1,23 @@
+# This file was auto-generated by Fern from our API Definition.
+
+import enum
+import typing
+
+T_Result = typing.TypeVar("T_Result")
+
+
+class SingleSeriesInsampleForecastModel(str, enum.Enum):
+    """
+    Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon.` We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.
+    """
+
+    TIMEGPT_1 = "timegpt-1"
+    TIMEGPT_1_LONG_HORIZON = "timegpt-1-long-horizon"
+
+    def visit(
+        self, timegpt_1: typing.Callable[[], T_Result], timegpt_1_long_horizon: typing.Callable[[], T_Result]
+    ) -> T_Result:
+        if self is SingleSeriesInsampleForecastModel.TIMEGPT_1:
+            return timegpt_1()
+        if self is SingleSeriesInsampleForecastModel.TIMEGPT_1_LONG_HORIZON:
+            return timegpt_1_long_horizon()