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arima_svm_laplace() is a way to generate a specification of a time series model that uses SVMs to improve modeling errors (residuals) on Exogenous Regressors. It works with both "automated" ARIMA (auto.arima) and standard ARIMA (arima). The main algorithms are:

  • Auto ARIMA + SVM Errors (engine = auto_arima_svm_laplace, default)

  • ARIMA + SVM Errors (engine = arima_svm_laplace)

Usage

arima_svm_laplace(
  mode = "regression",
  seasonal_period = NULL,
  non_seasonal_ar = NULL,
  non_seasonal_differences = NULL,
  non_seasonal_ma = NULL,
  seasonal_ar = NULL,
  seasonal_differences = NULL,
  seasonal_ma = NULL,
  cost = NULL,
  margin = NULL,
  laplace_sigma = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression".

seasonal_period

A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. See Fit Details below.

non_seasonal_ar

The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation.

non_seasonal_differences

The order of integration for non-seasonal differencing. Often denoted "d" in pdq-notation.

non_seasonal_ma

The order of the non-seasonal moving average (MA) terms. Often denoted "q" in pdq-notation.

seasonal_ar

The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation.

seasonal_differences

The order of integration for seasonal differencing. Often denoted "D" in PDQ-notation.

seasonal_ma

The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQ-notation.

cost

A positive number for the cost of predicting a sample within or on the wrong side of the margin

margin

A positive number for the epsilon in the SVM insensitive loss function (regression only)

laplace_sigma

sigma parameter for laplacian

sample_size

number for the number (or proportion) of data that is exposed to the fitting routine.