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FeaturizationConfig

Used by
Source code in docs_src/capo_forecast/types/featurization_config.py 
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class FeaturizationConfig(TypedDict, closed=True):
    forecast_frequency: "capo_forecast.types.frequency.Frequency"
    """<p>The frequency of predictions in a forecast.</p> <p>Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:</p> <ul> <li> <p>Minute - 1-59</p> </li> <li> <p>Hour - 1-23</p> </li> <li> <p>Day - 1-6</p> </li> <li> <p>Week - 1-4</p> </li> <li> <p>Month - 1-11</p> </li> <li> <p>Year - 1</p> </li> </ul> <p>Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".</p> <p>The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.</p> <p>When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.</p>"""
    forecast_dimensions: NotRequired[
        "capo_forecast.types.forecast_dimensions.ForecastDimensions"
    ]
    """<p>An array of dimension (field) names that specify how to group the generated forecast.</p> <p>For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a <code>store_id</code> field. If you want the sales forecast for each item by store, you would specify <code>store_id</code> as the dimension.</p> <p>All forecast dimensions specified in the <code>TARGET_TIME_SERIES</code> dataset don't need to be specified in the <code>CreatePredictor</code> request. All forecast dimensions specified in the <code>RELATED_TIME_SERIES</code> dataset must be specified in the <code>CreatePredictor</code> request.</p>"""
    featurizations: NotRequired["capo_forecast.types.featurizations.Featurizations"]
    """<p>An array of featurization (transformation) information for the fields of a dataset.</p>"""

featurizations instance-attribute 

featurizations: NotRequired[Featurizations]

An array of featurization (transformation) information for the fields of a dataset.

forecast_dimensions instance-attribute 

forecast_dimensions: NotRequired[ForecastDimensions]

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

forecast_frequency instance-attribute 

forecast_frequency: Frequency

The frequency of predictions in a forecast.

Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

  • Minute - 1-59

  • Hour - 1-23

  • Day - 1-6

  • Week - 1-4

  • Month - 1-11

  • Year - 1

Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.