ForecastingSettings Class

Forecasting settings for an AutoML Job.

Inheritance

azure.ai.ml.entities._mixins.RestTranslatableMixin

ForecastingSettings

Constructor

ForecastingSettings(*, country_or_region_for_holidays: str | None = None, cv_step_size: int | None = None, forecast_horizon: str | int | None = None, target_lags: str | int | List[int] | None = None, target_rolling_window_size: str | int | None = None, frequency: str | None = None, feature_lags: str | None = None, seasonality: str | int | None = None, use_stl: str | None = None, short_series_handling_config: str | None = None, target_aggregate_function: str | None = None, time_column_name: str | None = None, time_series_id_column_names: str | List[str] | None = None, features_unknown_at_forecast_time: str | List[str] | None = None)

Parameters

Name	Description
country_or_region_for_holidays Required	Optional[str] The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region code, for example 'US' or 'GB'.
cv_step_size Required	Optional[int] Number of periods between the origin_time of one CV fold and the next fold. For example, if n_step = 3 for daily data, the origin time for each fold will be three days apart.
forecast_horizon Required	Optional[Union[int, str]] The desired maximum forecast horizon in units of time-series frequency. The default value is 1. Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required. For more information on setting forecasting parameters, see Auto-train a time-series forecast model.
target_lags Required	Union[str, int, List[int]] The number of past periods to lag from the target column. By default the lags are turned off. When forecasting, this parameter represents the number of rows to lag the target values based on the frequency of the data. This is represented as a list or single integer. Lag should be used when the relationship between the independent variables and dependent variable do not match up or correlate by default. For example, when trying to forecast demand for a product, the demand in any month may depend on the price of specific commodities 3 months prior. In this example, you may want to lag the target (demand) negatively by 3 months so that the model is training on the correct relationship. For more information, see Auto-train a time-series forecast model. Note on auto detection of target lags and rolling window size. Please see the corresponding comments in the rolling window section. We use the next algorithm to detect the optimal target lag and rolling window size. Estimate the maximum lag order for the look back feature selection. In our case it is the number of periods till the next date frequency granularity i.e. if frequency is daily, it will be a week (7), if it is a week, it will be month (4). That values multiplied by two is the largest possible values of lags/rolling windows. In our examples, we will consider the maximum lag order of 14 and 8 respectively). Create a de-seasonalized series by adding trend and residual components. This will be used in the next step. Estimate the PACF - Partial Auto Correlation Function on the on the data from (2) and search for points, where the auto correlation is significant i.e. its absolute value is more then 1.96/square_root(maximal lag value), which correspond to significance of 95%. If all points are significant, we consider it being strong seasonality and do not create look back features. We scan the PACF values from the beginning and the value before the first insignificant auto correlation will designate the lag. If first significant element (value correlate with itself) is followed by insignificant, the lag will be 0 and we will not use look back features.
target_rolling_window_size Required	Optional[Union[str, int]] The number of past periods used to create a rolling window average of the target column. When forecasting, this parameter represents n historical periods to use to generate forecasted values, <= training set size. If omitted, n is the full training set size. Specify this parameter when you only want to consider a certain amount of history when training the model. If set to 'auto', rolling window will be estimated as the last value where the PACF is more then the significance threshold. Please see target_lags section for details.
frequency Required	Optional[str] Forecast frequency. When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. You can optionally set it to greater (but not lesser) than dataset frequency. We'll aggregate the data and generate the results at forecast frequency. For example, for daily data, you can set the frequency to be daily, weekly or monthly, but not hourly. The frequency needs to be a pandas offset alias. Please refer to pandas documentation for more information: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
feature_lags Required	Optional[str] Flag for generating lags for the numeric features with 'auto' or None.
seasonality Required	Optional[Union[int, str]] Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. If set to None, the time series is assumed non-seasonal which is equivalent to seasonality=1.
use_stl Required	Optional[str] Configure STL Decomposition of the time-series target column. use_stl can take three values: None (default) - no stl decomposition, 'season' - only generate season component and season_trend - generate both season and trend components.
short_series_handling_config Required	Optional[str] The parameter defining how if AutoML should handle short time series. Possible values: 'auto' (default), 'pad', 'drop' and None. auto short series will be padded if there are no long series, otherwise short series will be dropped. pad all the short series will be padded. drop all the short series will be dropped". None the short series will not be modified. If set to 'pad', the table will be padded with the zeroes and empty values for the regressors and random values for target with the mean equal to target value median for given time series id. If median is more or equal to zero, the minimal padded value will be clipped by zero. Input: Date numeric_value string target 2020-01-01 23 green 55 Output assuming minimal number of values is four: Date numeric_value string target 2019-12-29 0 NA 55.1 2019-12-30 0 NA 55.6 2019-12-31 0 NA 54.5 2020-01-01 23 green 55 Note: We have two parameters short_series_handling_configuration and legacy short_series_handling. When both parameters are set we are synchronize them as shown in the table below (short_series_handling_configuration and short_series_handling for brevity are marked as handling_configuration and handling respectively). handling handling configuration resulting handling resulting handlingconfiguration True auto True auto True pad True auto True drop True auto True None False None False auto False None False pad False None False drop False None False None False None
target_aggregate_function Required	str The function to be used to aggregate the time series target column to conform to a user specified frequency. If the target_aggregation_function is set, but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". The target column values are aggregated based on the specified operation. Typically, sum is appropriate for most scenarios. Numerical predictor columns in your data are aggregated by sum, mean, minimum value, and maximum value. As a result, automated ML generates new columns suffixed with the aggregation function name and applies the selected aggregate operation. For categorical predictor columns, the data is aggregated by mode, the most prominent category in the window. Date predictor columns are aggregated by minimum value, maximum value and mode. freq target_aggregation_function Data regularityfixing mechanism None (Default) None (Default) The aggregation is notapplied. If the validfrequency can not bedetermined the error willbe raised. Some Value None (Default) The aggregation is notapplied. If the numberof data points compliantto given frequency gridis less then 90% these pointswill be removed, otherwisethe error will be raised. None (Default) Aggregation function The error about missingfrequency parameteris raised. Some Value Aggregation function Aggregate to frequency usingprovided aggregation function.
time_column_name Required	Optional[str] The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.
time_series_id_column_names Required	Union[str, List[str]] The names of columns used to group a timeseries. It can be used to create multiple series. If time series id column names is not defined or the identifier columns specified do not identify all the series in the dataset, the time series identifiers will be automatically created for your dataset.
features_unknown_at_forecast_time Required	Optional[Union[str, List[str]]] The feature columns that are available for training but unknown at the time of forecast/inference. If features_unknown_at_forecast_time is set to an empty list, it is assumed that all the feature columns in the dataset are known at inference time. If this parameter is not set the support for future features is not enabled.

Keyword-Only Parameters

Name	Description
country_or_region_for_holidays Required
cv_step_size Required
forecast_horizon Required
target_lags Required
target_rolling_window_size Required
frequency Required
feature_lags Required
seasonality Required
use_stl Required
short_series_handling_config Required
target_aggregate_function Required
time_column_name Required
time_series_id_column_names Required
features_unknown_at_forecast_time Required