object SARIMA
Companion object for class SARIMA. Includes features related to differencing
and automated order selection.
- See also
www.jstatsoft.org/article/view/v027i03/v27i03.pdf
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- def difference(y: VectoD, d: Int, dd: Int, period: Int): (VectoD, VectoD)
Difference the time series.
Difference the time series. Return both the completely differenced time series and the intermediate (differenced once) time series (needed to scale back results later).
- y
the time series to be differenced
- d
the order of simple differencing
- dd
the order of seasonal differencing
- period
the seasonal period
- def differenceSeason(y_: VectoD, dd: Int, period: Int): VectoD
Difference for seasonality.
Difference for seasonality.
- y_
the time series to be seasonally differenced, this is usually the intermediate result after simple differencing.
- dd
the order of seasonal differencing
- period
the seasonal period
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- def transformBack(xp: VectoD, x_: VectoD, y: VectoD, d: Int, dd: Int, period: Int): VectoD
Transform the fitted values on the training data of a differenced time series back to the original scale.
Transform the fitted values on the training data of a differenced time series back to the original scale.
- xp
the vector of predictions/fitted values of a differenced time series
- x_
the intermediate result after differencing for trend, but before differencing for seasonality
- y
the original time series
- d
the order of simple differencing
- dd
the order of seasonal differencing
- period
the seasonal period
- See also
stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima
- def transformBackF(xf: VectoD, x_: VectoD, xx: VectoD, d: Int, dd: Int, period: Int, t: Int): VectoD
Transform the forecast values of a differenced time series back to the original scale.
Transform the forecast values of a differenced time series back to the original scale.
- xf
the vector of forecasted values of a differenced time series
- x_
the intermediate result after differencing for trend, but before differencing for seasonality
- xx
the original zero-mean time series
- d
the order of simple differencing
- dd
the order of seasonal differencing
- period
the seasonal period
- t
the time being forecasted (@see the 'forecast' method)
- See also
stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima
- def transformBackFSeason(xf: VectoD, x_: VectoD, dd: Int, period: Int, t: Int): VectoD
Transform the forecast values of a differenced time series back to the original scale.
Transform the forecast values of a differenced time series back to the original scale. Undo seasonal differencing only.
- xf
the vector of forecasted values of a differenced time series
- x_
the intermediate result after differencing for trend, but before differencing for seasonality
- dd
the order of seasonal differencing
- period
the seasonal period
- t
the time point being forecasted (@see the 'forecast' method)
- See also
stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima
- def transformBackSeason(xp: VectoD, x_: VectoD, dd: Int, period: Int): VectoD
Transform the fitted values on the training data of a differenced time series back to the original scale.
Transform the fitted values on the training data of a differenced time series back to the original scale. Undo seasonal differencing only.
- xp
the vector of predictions/fitted values of a differenced time series
- x_
the intermediate result after differencing for trend, but before differencing for seasonality
- dd
the order of seasonal differencing
- period
the seasonal period
- See also
stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima
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