Package software.amazon.awssdk.services.forecast.model

Interface FeaturizationConfig.Builder

    • Method Detail

      • forecastFrequency

        FeaturizationConfig.Builder forecastFrequency​(String forecastFrequency)

        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.

        Parameters:
        forecastFrequency - 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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • forecastDimensions

        FeaturizationConfig.Builder forecastDimensions​(Collection<String> 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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • forecastDimensions

        FeaturizationConfig.Builder forecastDimensions​(String... 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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • featurizations

        FeaturizationConfig.Builder featurizations​(Collection<Featurization> featurizations)

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

        Parameters:
        featurizations - An array of featurization (transformation) information for the fields of a dataset.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • featurizations

        FeaturizationConfig.Builder featurizations​(Featurization... featurizations)

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

        Parameters:
        featurizations - An array of featurization (transformation) information for the fields of a dataset.
        Returns:
        Returns a reference to this object so that method calls can be chained together.