# NDK_FARIMA_FORE

 int __stdcall NDK_FARIMA_FORE ( double * pData, size_t nSize, double mean, double sigma, double nIntegral, double * phis, size_t p, double * thetas, size_t q, size_t nStep, WORD retType, double * retVal )

Calculates the out-of-sample forecast statistics.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
[in] pData is the univariate time series data (a one dimensional array).
[in] nSize is the number of observations in pData.
[in] mean is the ARMA model mean (i.e. mu).
[in] sigma is the standard deviation of the model's residuals/innovations.
[in] nIntegral  is the model's integration order.
[in] phis are the parameters of the AR(p) component model (starting with the lowest lag).
[in] p is the number of elements in phis (order of AR component)
[in] thetas are the parameters of the MA(q) component model (starting with the lowest lag).
[in] q is the number of elements in thetas (order of MA component)
[in] nStep is the forecast time/horizon (expressed in terms of steps beyond end of the time series).
[in] retType  is a switch to select the type of value returned
Order   Description
1 Mean forecast value (default)
2 Forecast standard error (aka local volatility)
3 Volatility term structure
4 Lower limit of the forecast confidence interval
5 Upper limit of the forecast confidence interval
[in] alpha  is the statistical significance level. If missing, a default of 5% is assumed.
[out] retVal  is the calculated forecast value
Remarks
1. The underlying model is described here.
2. The time series is homogeneous or equally spaced
3. The time series may include missing values (e.g. NaN) at either end.
Requirements