Time series projects

Time series projects, like OTV projects, use datetime partitioning, and all the workflow changes that apply to other datetime partitioned projects also apply to them. Unlike other projects, time series projects produce different types of models which forecast multiple future predictions instead of an individual prediction for each row.

DataRobot uses a general time series framework to configure how time series features are created and what future values the models will output. This framework consists of a Forecast Point (defining a time a prediction is being made), a Feature Derivation Window (a rolling window used to create features), and a Forecast Window (a rolling window of future values to predict). These components are described in more detail below.

Time series projects will automatically transform the dataset provided in order to apply this framework. During the transformation, DataRobot uses the Feature Derivation Window to derive time series features (such as lags and rolling statistics), and uses the Forecast Window to provide examples of forecasting different distances in the future (such as time shifts). After project creation, a new dataset and a new feature list are generated and used to train the models. This process is reapplied automatically at prediction time as well in order to generate future predictions based on the original data features.

The time_unit and time_step used to define the Feature Derivation and Forecast Windows are taken from the datetime partition column, and can be retrieved for a given column in the input data by looking at the corresponding attributes on the datarobot.models.Feature object. If windows_basis_unit is set to ROW, then Feature Derivation and Forecast Windows will be defined using number of the rows.

Setting up a time series project

To set up a time series project, follow the standard datetime partitioning workflow and use the six new time series specific parameters on the datarobot.DatetimePartitioningSpecification object:

use_time_series

: bool, set this to True to enable time series for the project.

default_to_known_in_advance

: bool, set this to True to default to treating all features as known in advance, or a priori, features. Otherwise, they will not be handled as known in advance features. Individual features can be set to a value different than the default by using the featureSettings parameter. See the prediction documentation for more information.

default_to_do_not_derive

: bool, set this to True to default to excluding all features from feature derivation. Otherwise, they will not be excluded and will be included in the feature derivation process. Individual features can be set to a value different than the default by using the featureSettings parameter.

feature_derivation_window_start

: int, specifies how many units of the windows_basis_unit from the forecast point into the past is the start of the feature derivation window

feature_derivation_window_end

: int, specifies how many units of the windows_basis_unit from the forecast point into the past is the end of the feature derivation window

forecast_window_start

: int, specifies how many units of the windows_basis_unit from the forecast point into the future is the start of the forecast window

forecast_window_end

: int, specifies how many units of the windows_basis_unit from the forecast point into the future is the end of the forecast window

windows_basis_unit

: string, set this to ROW to define feature derivation and forecast windows in terms of the rows, rather than time units. If omitted, will default to the detected time unit (one of the datarobot.enums.TIME_UNITS).

feature_settings

: list of FeatureSettings specifying per feature settings, can be left unspecified

Feature Derivation Window

The Feature Derivation window represents the rolling window that is used to derive time series features and lags, relative to the Forecast Point. It is defined in terms of feature_derivation_window_start and feature_derivation_window_end which are integer values representing datetime offsets in terms of the time_unit (e.g. hours or days).

The Feature Derivation Window start and end must be less than or equal to zero, indicating they are positioned before the forecast point. Additionally, the window must be specified as an integer multiple of the time_step which defines the expected difference in time units between rows in the data.

The window is closed, meaning the edges are considered to be inside the window.

Forecast window

The Forecast Window represents the rolling window of future values to predict, relative to the Forecast Point. It is defined in terms of the forecast_window_start and forecast_window_end, which are positive integer values indicating datetime offsets in terms of the time_unit (e.g. hours or days).

The Forecast Window start and end must be positive integers, indicating they are positioned after the forecast point. Additionally, the window must be specified as an integer multiple of the time_step which defines the expected difference in time units between rows in the data.

The window is closed, meaning the edges are considered to be inside the window.

Multiseries projects

Certain time series problems represent multiple separate series of data, e.g. “I have five different stores that all have different customer bases. I want to predict how many units of a particular item will sell, and account for the different behavior of each store”. When setting up the project, a column specifying series ids must be identified, so that each row from the same series has the same value in the multiseries id column.

Using a multiseries id column changes which partition columns are eligible for time series, as each series is required to be unique and regular, instead of the entire partition column being required to have those properties. In order to use a multiseries id column for partitioning, a detection job must first be run to analyze the relationship between the partition and multiseries id columns. If needed, it will be automatically triggered by calling datarobot.models.Feature.get_multiseries_properties() on the desired partition column. The previously computed multiseries properties for a particular partition column can then be accessed via that method. The computation will also be automatically triggered when calling datarobot.DatetimePartitioning.generate() or datarobot.models.Project.analyze_and_model() with a multiseries id column specified.

Note that currently only one multiseries id column is supported, but all interfaces accept lists of id columns to ensure multiple id columns will be able to be supported in the future.

In order to create a multiseries project:

1. Set up a datetime partitioning specification with the desired partition column and multiseries id columns.

2. (Optionally) Use datarobot.models.Feature.get_multiseries_properties() to confirm the inferred time step and time unit of the partition column when used with the specified multiseries id column.

3. (Optionally) Specify the multiseries id column in order to preview the full datetime partitioning settings using datarobot.DatetimePartitioning.generate().

4. Specify the multiseries id column when sending the target and partitioning settings via datarobot.models.Project.analyze_and_model().

project = dr.Project.create('path/to/multiseries.csv', project_name='my multiseries project')
partitioning_spec = dr.DatetimePartitioningSpecification(
    'timestamp', use_time_series=True, multiseries_id_columns=['multiseries_id']
)

# manually confirm time step and time unit are as expected
datetime_feature = dr.Feature.get(project.id, 'timestamp')
multiseries_props = datetime_feature.get_multiseries_properties(['multiseries_id'])
print(multiseries_props)

# manually check out the partitioning settings like feature derivation window and backtests
# to make sure they make sense before moving on
full_part = dr.DatetimePartitioning.generate(project.id, partitioning_spec)
print(full_part.feature_derivation_window_start, full_part.feature_derivation_window_end)
print(full_part.to_dataframe())

# As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``.
 # The spec options can be passed individually:
 project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', multiseries_id_columns=['series_id'])
 # Or the whole spec object can be passed:
 project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec)

# finalize the project and start the autopilot
project.analyze_and_model('target', partitioning_method=partitioning_spec)

You can also access optimized partitioning in the API where the target over time is inspected to ensure that the default backtests cover regions of interest and adjust backtests avoid common problems with missing target values or partitions with single values (e.g. zero-inflated datasets). In this case you need to pass the target column when generating the partitioning specification (either by calling DatetimePartitioning.generate or Project.set_datetime_partitioning) and then pass the full partitioning specification when starting autopilot (if Project.set_datetime_partitioning is not used).

project = dr.Project.create('path/to/multiseries.csv', project_name='my multiseries project')
partitioning_spec = dr.DatetimePartitioningSpecification(
    'timestamp', use_time_series=True, multiseries_id_columns=['multiseries_id']
)

# Pass the target column to generate optimized partitions
full_part = dr.DatetimePartitioning.generate(project.id, partitioning_spec, 'target')

# Or, as of v3.0, call ``Project.set_datetime_partitioning`` after specifying the project target
# to generate optimized partitions.
project.target = 'target'
project.set_datetime_partitioning(datetime_partition_spec=partitioning_spec)

# finalize the project and start the autopilot, passing in the full partitioning spec
# (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``)
project.analyze_and_model('target', partitioning_method=full_part.to_specification())

Feature settings

datarobot.FeatureSettings constructor receives feature_name and settings. For now settings known_in_advance and do_not_derive are supported.

# I have 10 features, 8 of them are known in advance and two are not
# Also, I do not want to derive new features from previous_day_sales
not_known_in_advance_features = ['previous_day_sales', 'amount_in_stock']
do_not_derive_features = ['previous_day_sales']
feature_settings = [dr.FeatureSettings(feat_name, known_in_advance=False) for feat_name in not_known_in_advance_features]
feature_settings += [dr.FeatureSettings(feat_name, do_not_derive=True) for feat_name in do_not_derive_features]
spec = dr.DatetimePartitioningSpecification(
    # ...
    default_to_known_in_advance=True,
    feature_settings=feature_settings
)

Modeling data and time series features

In time series projects, a new set of modeling features is created after setting the partitioning options. If a featurelist is specified with the partitioning options, it will be used to select which features should be used to derived modeling features; if a featurelist is not specified, the default featurelist will be used.

These features are automatically derived from those in the project’s dataset and are the features used for modeling - note that the Project methods get_featurelists and get_modeling_featurelists will return different data in time series projects. Modeling featurelists are the ones that can be used for modeling and will be accepted by the backend, while regular featurelists will continue to exist but cannot be used. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, modeling and regular features and featurelists will behave the same.

Restoring discarded features

datarobot.models.restore_discarded_features.DiscardedFeaturesInfo can be used to get and restore features that have been removed by the time series feature generation and reduction functionality.

project = Project(project_id)
discarded_feature_info = project.get_discarded_features()
restored_features_info = project.restore_discarded_features(discarded_features_info.features)

Making predictions

Prediction datasets are uploaded as normal. However, when uploading a prediction dataset, a new parameter forecast_point can be specified. The forecast point of a prediction dataset identifies the point in time relative which predictions should be generated, and if one is not specified when uploading a dataset, the server will choose the most recent possible forecast point. The forecast window specified when setting the partitioning options for the project determines how far into the future from the forecast point predictions should be calculated.

To simplify the predictions process, starting in version v2.20 a forecast point or prediction start and end dates can be specified when requesting predictions, instead of being specified at dataset upload. Upon uploading a dataset, DataRobot will calculate the range of dates available for use as a forecast point or for batch predictions. To that end, Predictions objects now also contain the following new fields:

• forecast_point: The default point relative to which predictions will be generated

• predictions_start_date: The start date for bulk historical predictions.

• predictions_end_date: The end date for bulk historical predictions.

Similar settings are provided as part of the batch prediction API and the real-time prediction API to make predictions using deployed time series models.

datarobot.models.BatchPredictionJob.score

When setting up a time series project, input features could be identified as known-in-advance features. These features are not used to generate lags, and are expected to be known for the rows in the forecast window at predict time (e.g. “how much money will have been spent on marketing”, “is this a holiday”).

Enough rows of historical data must be provided to cover the span of the effective Feature Derivation Window (which may be longer than the project’s Feature Derivation Window depending on the differencing settings chosen). The effective Feature Derivation Window of any model can be checked via the effective_feature_derivation_window_start and effective_feature_derivation_window_end attributes of a DatetimeModel.

When uploading datasets to a time series project, the dataset might look something like the following, where “Time” is the datetime partition column, “Target” is the target column, and “Temp.” is an input feature. If the dataset was uploaded with a forecast point of “2017-01-08” and the effective feature derivation window start and end for the model are -5 and -3 and the forecast window start and end were set to 1 and 3, then rows 1 through 3 are historical data, row 6 is the forecast point, and rows 7 though 9 are forecast rows that will have predictions when predictions are computed.

Row, Time, Target, Temp.
1, 2017-01-03, 16443, 72
2, 2017-01-04, 3013, 72
3, 2017-01-05, 1643, 68
4, 2017-01-06, ,
5, 2017-01-07, ,
6, 2017-01-08, ,
7, 2017-01-09, ,
8, 2017-01-10, ,
9, 2017-01-11, ,

On the other hand, if the project instead used “Holiday” as an a priori input feature, the uploaded dataset might look like the following:

Row, Time, Target, Holiday
1, 2017-01-03, 16443, TRUE
2, 2017-01-04, 3013, FALSE
3, 2017-01-05, 1643, FALSE
4, 2017-01-06, , FALSE
5, 2017-01-07, , FALSE
6, 2017-01-08, , FALSE
7, 2017-01-09, , TRUE
8, 2017-01-10, , FALSE
9, 2017-01-11, , FALSE

Calendars

You can upload a calendar file containing a list of events relevant to your dataset. When provided, DataRobot automatically derives and creates time series features based on the calendar events (e.g., time until the next event, labeling the most recent event).

The calendar file:

• Should span the entire training data date range, as well as all future dates in which model will be forecasting.

• Must be in csv or xlsx format with a header row.

• Must have one date column which has values in the date-only format YYY-MM-DD (i.e., no hour, month, or second).

• Can optionally include a second column that provides the event name or type.

• Can optionally include a series ID column which specifies which series an event is applicable to. This column name must match the name of the column set as the series ID.

  • Multiseries ID columns are used to add an ability to specify different sets of events for different series, e.g. holidays for different regions.

  • Values of the series ID may be absent for specific events. This means that the event is valid for all series in project dataset (e.g. New Year’s Day is a holiday in all series in the example below).

  • If a multiseries ID column is not provided, all listed events will be applicable to all series in the project dataset.

• Cannot be updated in an active project. You must specify all future calendar events at project start. To update the calendar file, you will have to train a new project.

An example of a valid calendar file:

Date,        Name
2019-01-01,  New Year's Day
2019-02-14,  Valentine's Day
2019-04-01,  April Fools
2019-05-05,  Cinco de Mayo
2019-07-04,  July 4th

An example of a valid multiseries calendar file:

Date,        Name,                   Country
2019-01-01,  New Year's Day,
2019-05-27,  Memorial Day,           USA
2019-07-04,  July 4th,               USA
2019-11-28,  Thanksgiving,           USA
2019-02-04,  Constitution Day,       Mexico
2019-03-18,  Benito Juárez's birth,  Mexico
2019-12-25,  Christmas Day,

Once created, a calendar can be used with a time series project by specifying the calendar_id field in the datarobot.DatetimePartitioningSpecification object for the project:

import datarobot as dr

# create the project
project = dr.Project.create('input_data.csv')
# create the calendar
calendar = dr.CalendarFile.create('calendar_file.csv')

# specify the calendar_id in the partitioning specification
datetime_spec = dr.DatetimePartitioningSpecification(
    use_time_series=True,
    datetime_partition_column='date'
    calendar_id=calendar.id
)

# As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``.
# The spec options can be passed individually:
project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', calendar_id=calendar.id)
# Or the whole spec object can be passed:
project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec)

# start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``)
project.analyze_and_model(
    target='project target',
    partitioning_method=datetime_spec
)

As of version v2.23 it is possible to ask DataRobot to generate a calendar file for you using CalendarFile.create_calendar_from_country_code. This method allows you to provide a country code specifying which country’s holidays to use in generating the calendar, along with a start and end date indicating the bounds of the calendar. Allowed country codes can be retrieved using CalendarFile.get_allowed_country_codes. See the following code block for example usage:

import datarobot as dr
from datetime import datetime

# create the project
project = dr.Project.create('input_data.csv')
# retrieve the allowed country codes and use the first one
country_code = dr.CalendarFile.get_allowed_country_codes()[0]['code']
calendar = dr.CalendarFile.create_calendar_from_country_code(
    country_code, datetime(2018, 1, 1), datetime(2018, 7, 4)
)
# specify the calendar_id in the partitioning specification
datetime_spec = dr.DatetimePartitioningSpecification(
    use_time_series=True,
    datetime_partition_column='date'
    calendar_id=calendar.id
)

# As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``.
# The spec options can be passed individually:
project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', calendar_id=calendar.id)
# Or the whole spec object can be passed:
project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec)

# Start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``)
project.analyze_and_model(
    target='project target',
    partitioning_method=datetime_spec
)

Datetime trend plots

As a version v2.25, it is possible to retrieve Datetime Trend Plots for time series models to estimate the accuracy of the model. This includes Accuracy over Time and Forecast vs Actual for supervised projects, and Anomaly over Time for unsupervised projects. You can retrieve respective plots using following methods:

• DatetimeModel.get_accuracy_over_time_plot

• DatetimeModel.get_forecast_vs_actual_plot

• DatetimeModel.get_anomaly_over_time_plot

By default, the plots would be automatically computed when accessed via retrieval methods. You can compute Datetime Trend Plots separately using a common method DatetimeModel.compute_datetime_trend_plots.

In addition, you can retrieve the respective detailed metadata for each plot type:

• DatetimeModel.get_accuracy_over_time_plots_metadata

• DatetimeModel.get_forecast_vs_actual_plots_metadata

• DatetimeModel.get_anomaly_over_time_plots_metadata

And the preview plots:

• DatetimeModel.get_accuracy_over_time_plot_preview

• DatetimeModel.get_forecast_vs_actual_plot_preview

• DatetimeModel.get_anomaly_over_time_plot_preview

Prediction intervals

For each model, prediction intervals estimate the range of values DataRobot expects actual values of the target to fall within. They are similar to a confidence interval of a prediction, but are based on the residual errors measured during the backtesting for the selected model.

Note that because calculation depends on the backtesting values, prediction intervals are not available for predictions on models that have not had all backtests completed. To that end, note that creating a prediction with prediction intervals through the API will automatically complete all backtests if they were not already completed. For start-end retrained models, the parent model will be used for backtesting. Additionally, prediction intervals are not available when the number of points per forecast distance is less than 10, due to insufficient data.

In a prediction request, users can specify a prediction interval’s size, which specifies the desired probability of actual values falling within the interval range. Larger values are less precise, but more conservative. For example, specifying a size of 80 will result in a lower bound of 10% and an upper bound of 90%. More generally, for a specific prediction_intervals_size, the upper and lower bounds will be calculated as follows:

• prediction_interval_upper_bound = 50% + (prediction_intervals_size / 2)

• prediction_interval_lower_bound = 50% - (prediction_intervals_size / 2)

Prediction intervals can be calculated for a DatetimeModel using the DatetimeModel.calculate_prediction_intervals method. Users can also retrieve which intervals have already been calculated for the model using the DatetimeModel.get_calculated_prediction_intervals method.

To view prediction intervals data for a prediction, the prediction needs to have been created using the DatetimeModel.request_predictions method and specifying include_prediction_intervals = True. The size for the prediction interval can be specified with the prediction_intervals_size parameter for the same function, and will default to 80 if left unspecified. Specifying either of these fields will result in prediction interval bounds being included in the retrieved prediction data for that request (see the Predictions class for retrieval methods). Note that if the specified interval size has not already been calculated, this request will automatically calculate the specified size.

Prediction intervals are also supported for time series model deployments, and should be specified in deployment settings if desired. Use Deployment.get_prediction_intervals_settings to retrieve current prediction intervals settings for a deployment, and Deployment.update_prediction_intervals_settings to update prediction intervals settings for a deployment.

Partial history predictions

As of version v2.24 it is possible to ask DataRobot to allow to make predictions with incomplete historical data multiseries regression projects. To make predictions in regular project user has to provide enough data for the feature derivation. By setting the datetime partitioning attribute allow_partial_history_time_series_predictions to true (datarobot.DatetimePartitioningSpecification object), the project would be created that allow to make such predictions. The number of models are significantly smaller compared to regular multiseries model, but they are designed to make predictions on unseen series with reasonable accuracy.

External baseline predictions

As of version v2.26 it is possible to ask DataRobot to scale accuracy metric by external predictions. Users can upload data into a Dataset (see Dataset documentation) and compare the external time series predictions with DataRobot models’ accuracy performance. To use the external predictions dataset in the autopilot, the dataset must be validated first (see Project.validate_external_time_series_baseline). Once the dataset is validated, it can be used with a time series project by specifying external_time_series_baseline_dataset_id field in AdvancedOptions and passes the advanced options to the project. See the following code block for example usage:

import datarobot as dr
from datarobot.helpers import AdvancedOptions
from datarobot.models import Dataset

# create the project
project = dr.Project.create('input_data.csv')

# prepare datetime partitioning for external baseline validation
datetime_spec = dr.DatetimePartitioningSpecification(
    use_time_series=True,
    datetime_partition_column='date',
    multiseries_id_columns=['series_id'],
)
datetime_partitioning = dr.DatetimePartitioning.generate(
    project_id=project.id,
    spec=datetime_spec,
    target='target',
)

# create external baseline prediction dataset from local file
external_baseline_dataset = Dataset.create_from_file(file_path='external_predictions.csv')

# validate the external baseline prediction dataset
validation_info = project.validate_external_time_series_baseline(
    catalog_version_id=external_baseline_dataset.version_id,
    target='target',
    datetime_partitioning=datetime_partitioning,
)
print(
    'External baseline predictions passes validation check:',
    validation_info.is_external_baseline_dataset_valid
)

# As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``.
# The spec options can be passed individually:
project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', multiseries_id_columns=['series_id'])
# Or the whole spec object can be passed:
project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec)

# As of v3.0, add the validated dataset version id into advanced options
project.set_options(
    external_time_series_baseline_dataset_id=external_baseline_dataset.version_id
)

# start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` and ``Project.set_options`` were not used)
project.analyze_and_model(
    target='target',
    partitioning_method=datetime_spec
    advanced_options=AdvancedOptions(external_time_series_baseline_dataset_id)
)

Time Series Data Prep

As of version v2.27 it is possible to prepare a dataset for time series modeling in the AI catalog using the API client. Users can upload unprepped modeling data into a Dataset (see Dataset documentation) and the prep the data set for time series modeling by aggregating data to a regular time step and filling gaps via a generated Spark SQL query in the AI catalog. Once the dataset is uploaded, the time series data prep query generator can be created using DataEngineQueryGenerator.create. As of version v3.1 convenience methods have been added to streamline the process of applying time series data prep for predictions. See the following code block for example usage:

import datarobot as dr
from datarobot.models.data_engine_query_generator import (
    QueryGeneratorDataset,
    QueryGeneratorSettings,
)
from datetime import datetime

# upload the dataset to the AI Catalog
dataset = dr.Dataset.create_from_file('input_data.csv')

# create a time series data prep query generator
query_generator_dataset = QueryGeneratorDataset(
    alias='input_data_csv',
    dataset_id=dataset.id,
    dataset_version_id=dataset.version_id,
)
query_generator_settings = QueryGeneratorSettings(
    datetime_partition_column="date",
    time_unit="DAY",
    time_step=1,
    default_numeric_aggregation_method="sum",
    default_categorical_aggregation_method="mostFrequent",
    target="y",
    multiseries_id_columns=["id"],
    default_text_aggregation_method="concat",
    start_from_series_min_datetime=True,
    end_to_series_max_datetime=True,
)
query_generator = dr.DataEngineQueryGenerator.create(
    generator_type='TimeSeries',
    datasets = [query_generator_dataset],
    generator_settings=query_generator_settings,
)

# prep the training dataset
training_dataset = query_generator.create_dataset()

# create a project
project = dr.Project.create_from_dataset(training_dataset.id, project_name='prepped_dataset')

# set up datetime partitioning, target, and train model(s)
partitioning_spec = dr.DatetimePartitioningSpecification(
    datetime_partition_column='date', use_time_series=True
)
project.analyze_and_model(target='y', mode='manual', partitioning_method=partitioning_spec)
blueprints = project.get_blueprints()
model_job = project.train_datetime(blueprints[0].id)
model = model_job.get_result_when_complete()

# query generator can be retrieved from the project if necessary
# query_generator = dr.DataEngineQueryGenerator.get(project.query_generator_id)

# prep and upload a prediction dataset to the project
prediction_dataset = query_generator.prepare_prediction_dataset(
    'prediction_data.csv', project.id
)

# make predictions within the project
# Either forecast point or predictions start/end dates must be specified
model.request_predictions(prediction_dataset.id, forecast_point=datetime(2023, 1, 1))

# query generator can be retrieved from a deployed model via project if necessary
# deployment = dr.Deployment.get(deployment_id)
# project = dr.Project.get(deployment.model['project_id'])
# query_generator = dr.DataEngineQueryGenerator.get(project.query_generator_id)

# Deploy the model
prediction_servers = dr.PredictionServer.list()
deployment = dr.Deployment.create_from_learning_model(
    model.id, 'prepped_deployment', default_prediction_server_id=prediction_servers[0].id
)

# Make batch predictions from batch prediction job, supports localFile or dataset for intake
# and all types for output
timeseries_settings = {'type': 'forecast', 'forecast_point': datetime(2023, 1, 1)}
intake_settings = {'type': 'localFile', 'file': 'prediction_data.csv'}
output_settings = {'type': 'localFile', 'path': 'predictions_out.csv'}
batch_predictions_job = dr.BatchPredictionJob.apply_time_series_data_prep_and_score(
    deployment, intake_settings, timeseries_settings, output_settings=output_settings
)