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scalation.analytics.forecaster

SimpleRollingValidation

object SimpleRollingValidation

The SimpleRollingValidation object provides '1'-fold rolling validations, e.g., for 'm = 1200' and 'k = 1', 'kt = 5':

1: tr(ain) 0 until 600, te(st) 600 until 1200

In rolling validation for this case, each retraining dataset has 600 instances, while the testing dataset has 600. Re-training occurs before every 'kt = 2' forecasts are made.

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  6. def crossValidate(model: PredictorMat with ForecasterMat, kt_: Int = 5, h: Int = 1): Array[Statistic]

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset.

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset. The test dataset is defined by a range of indices (test start until start + 'te_size') and 'tr_size' of the data before this is the training dataset. ------------------------------------------------------------------------- This version is for models that have an 'x' component and 'y' component, e.g., Regression4TS.

    model

    the forecastering model being used (e.g., QuadRegression4TS)

    kt_

    the frequency of re-training (number of forecasts to make before re-training) (defaults to 5)

    h

    the forecasting horizon, number of steps ahead to produce forecasts (defaults to 1)

    See also

    PredictorMat with ForecasterMat for the types of models

    analytics.package.scala for 'chopr' and 'shift_r' methods

  7. def crossValidate2(model: ForecasterVec, kt_: Int = 5, h: Int = 1): Array[Statistic]

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset.

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset. The test dataset is defined by a range of indices (test start until start + 'te_size') and 'tr_size' of the data before this is the training dataset. ------------------------------------------------------------------------- This version is for models that have no 'x' component, only the 'y' component, e.g., AR.

    model

    the forecastering model being used (e.g., ARIMA)

    kt_

    the frequency of re-training (number of forecasts to make before re-training) (defaults to 5)

    h

    the forecasting horizon, number of steps ahead to produce forecasts (defaults to 1)

    See also

    ForecasterVec for the types of models

    analytics.package.scala for 'chopr' and 'shift_r' methods

  8. def crossValidate3(model: PredictorMat2 with ForecasterMat, kt_: Int = 50, h: Int = 1): Array[Statistic]

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset.

    Use rolling '1'-fold cross-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a test dataset and a training dataset. The test dataset is defined by a range of indices (test start until start + 'te_size') and 'tr_size' of the data before this is the training dataset. ------------------------------------------------------------------------- This version is for models that have an 'x' component and 'y' component, e.g., NeuralNet_3L1_4TS.

    model

    the forecastering model being used (e.g., NeuralNet_3L1_4TS)

    kt_

    the frequency of re-training (number of forecasts to make before re-training) (defaults to 50)

    h

    the forecasting horizon, number of steps ahead to produce forecasts (defaults to 1)

    See also

    PredictorMat2 with ForecasterMat for the types of models

    analytics.package.scala for 'chopr' and 'shift_r' methods

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  19. def trSize(m: Int): Int

    Calculate the size (number of instances) for a training dataset.

    Calculate the size (number of instances) for a training dataset.

    m

    the size of the full dataset

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