> ## Documentation Index > Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > Distributed pipeline encapsulation # Distributed Forecast *** source ### DistributedMLForecast > ```text theme={null} > DistributedMLForecast (models, freq:Union[int,str], > lags:Optional[Iterable[int]]=None, lag_transforms: > Optional[Dict[int,List[Union[Callable,Tuple[Callab > le,Any]]]]]=None, date_features:Optional[Iterable[ > Union[str,Callable]]]=None, num_threads:int=1, tar > get_transforms:Optional[List[Union[mlforecast.targ > et_transforms.BaseTargetTransform,mlforecast.targe > t_transforms._BaseGroupedArrayTargetTransform]]]=N > one, engine=None, > num_partitions:Optional[int]=None, > lag_transforms_namer:Optional[Callable]=None) > ``` *Multi backend distributed pipeline* *** source ### DistributedMLForecast.fit > ```text theme={null} > DistributedMLForecast.fit (df:~AnyDataFrame, id_col:str='unique_id', > time_col:str='ds', target_col:str='y', > static_features:Optional[List[str]]=None, > dropna:bool=True, > keep_last_n:Optional[int]=None) > ``` *Apply the feature engineering and train the models.* | | **Type** | **Default** | **Details** | | ---------------- | ------------------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------- | | df | AnyDataFrame | | Series data in long format. | | id\_col | str | unique\_id | Column that identifies each serie. | | time\_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. | | target\_col | str | y | Column that contains the target. | | static\_features | Optional | None | Names of the features that are static and will be repeated when forecasting. | | dropna | bool | True | Drop rows with missing values produced by the transformations. | | keep\_last\_n | Optional | None | Keep only these many records from each serie for the forecasting step. Can save time and memory if your features allow it. | | **Returns** | **DistributedMLForecast** | | **Forecast object with series values and trained models.** | *** source ### DistributedMLForecast.predict > ```text theme={null} > DistributedMLForecast.predict (h:int, > before_predict_callback:Optional[Callable] > =None, after_predict_callback:Optional[Cal > lable]=None, X_df:Optional[pandas.core.fra > me.DataFrame]=None, > new_df:Optional[~AnyDataFrame]=None, > ids:Optional[List[str]]=None) > ``` *Compute the predictions for the next `horizon` steps.* | | **Type** | **Default** | **Details** | | ------------------------- | ---------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | h | int | | Forecast horizon. | | before\_predict\_callback | Optional | None | Function to call on the features before computing the predictions.
This function will take the input dataframe that will be passed to the model for predicting and should return a dataframe with the same structure.
The series identifier is on the index. | | after\_predict\_callback | Optional | None | Function to call on the predictions before updating the targets.
This function will take a pandas Series with the predictions and should return another one with the same structure.
The series identifier is on the index. | | X\_df | Optional | None | Dataframe with the future exogenous features. Should have the id column and the time column. | | new\_df | Optional | None | Series data of new observations for which forecasts are to be generated.
This dataframe should have the same structure as the one used to fit the model, including any features and time series data.
If `new_df` is not None, the method will generate forecasts for the new observations. | | ids | Optional | None | List with subset of ids seen during training for which the forecasts should be computed. | | **Returns** | **AnyDataFrame** | | **Predictions for each serie and timestep, with one column per model.** | *** source ### DistributedMLForecast.save > ```text theme={null} > DistributedMLForecast.save (path:str) > ``` *Save forecast object* | | **Type** | **Details** | | ----------- | -------- | ----------------------------------------- | | path | str | Directory where artifacts will be stored. | | **Returns** | **None** | | *** source ### DistributedMLForecast.load > ```text theme={null} > DistributedMLForecast.load (path:str, engine) > ``` *Load forecast object* | | **Type** | **Details** | | ----------- | ------------------------- | ----------------------------------------------------------------------- | | path | str | Directory with saved artifacts. | | engine | fugue execution engine | Dask Client, Spark Session, etc to use for the distributed computation. | | **Returns** | **DistributedMLForecast** | | *** source ### DistributedMLForecast.update > ```text theme={null} > DistributedMLForecast.update (df:pandas.core.frame.DataFrame) > ``` *Update the values of the stored series.* | | **Type** | **Details** | | ----------- | --------- | -------------------------------- | | df | DataFrame | Dataframe with new observations. | | **Returns** | **None** | | *** source ### DistributedMLForecast.to\_local > ```text theme={null} > DistributedMLForecast.to_local () > ``` \*Convert this distributed forecast object into a local one This pulls all the data from the remote machines, so you have to be sure that it fits in the scheduler/driver. If you’re not sure use the save method instead.\* *** source ### DistributedMLForecast.preprocess > ```text theme={null} > DistributedMLForecast.preprocess (df:~AnyDataFrame, > id_col:str='unique_id', > time_col:str='ds', target_col:str='y', > static_features:Optional[List[str]]=Non > e, dropna:bool=True, > keep_last_n:Optional[int]=None) > ``` *Add the features to `data`.* | | **Type** | **Default** | **Details** | | ---------------- | ---------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------- | | df | AnyDataFrame | | Series data in long format. | | id\_col | str | unique\_id | Column that identifies each serie. | | time\_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. | | target\_col | str | y | Column that contains the target. | | static\_features | Optional | None | Names of the features that are static and will be repeated when forecasting. | | dropna | bool | True | Drop rows with missing values produced by the transformations. | | keep\_last\_n | Optional | None | Keep only these many records from each serie for the forecasting step. Can save time and memory if your features allow it. | | **Returns** | **AnyDataFrame** | | **`df` with added features.** | *** source ### DistributedMLForecast.cross\_validation > ```text theme={null} > DistributedMLForecast.cross_validation (df:~AnyDataFrame, n_windows:int, > h:int, id_col:str='unique_id', > time_col:str='ds', > target_col:str='y', > step_size:Optional[int]=None, sta > tic_features:Optional[List[str]]= > None, dropna:bool=True, > keep_last_n:Optional[int]=None, > refit:bool=True, before_predict_c > allback:Optional[Callable]=None, > after_predict_callback:Optional[C > allable]=None, > input_size:Optional[int]=None) > ``` *Perform time series cross validation. Creates `n_windows` splits where each window has `h` test periods, trains the models, computes the predictions and merges the actuals.* | | **Type** | **Default** | **Details** | | ------------------------- | ---------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | df | AnyDataFrame | | Series data in long format. | | n\_windows | int | | Number of windows to evaluate. | | h | int | | Number of test periods in each window. | | id\_col | str | unique\_id | Column that identifies each serie. | | time\_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. | | target\_col | str | y | Column that contains the target. | | step\_size | Optional | None | Step size between each cross validation window. If None it will be equal to `h`. | | static\_features | Optional | None | Names of the features that are static and will be repeated when forecasting. | | dropna | bool | True | Drop rows with missing values produced by the transformations. | | keep\_last\_n | Optional | None | Keep only these many records from each serie for the forecasting step. Can save time and memory if your features allow it. | | refit | bool | True | Retrain model for each cross validation window.
If False, the models are trained at the beginning and then used to predict each window. | | before\_predict\_callback | Optional | None | Function to call on the features before computing the predictions.
This function will take the input dataframe that will be passed to the model for predicting and should return a dataframe with the same structure.
The series identifier is on the index. | | after\_predict\_callback | Optional | None | Function to call on the predictions before updating the targets.
This function will take a pandas Series with the predictions and should return another one with the same structure.
The series identifier is on the index. | | input\_size | Optional | None | Maximum training samples per serie in each window. If None, will use an expanding window. | | **Returns** | **AnyDataFrame** | | **Predictions for each window with the series id, timestamp, target value and predictions from each model.** |