NDK_SARIMAX_FITTED

int __stdcall NDK_SARIMAX_FITTED ( double *  pData,
double **  pFactors,
size_t  nSize,
size_t  nFactors,
double *  fBetas,
double  mean,
double  sigma,
WORD  nIntegral,
double *  phis,
size_t  p,
double *  thetas,
size_t  q,
WORD  nSIntegral,
WORD  nSPeriod,
double *  sPhis,
size_t  sP,
double *  sThetas,
size_t  sQ,
FIT_RETVAL_FUNC  retType  
)

Returns an array of cells for the fitted values (i.e. mean, volatility and residuals)

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in,out] pData  is the univariate time series data (a one dimensional array).
[in] pFactors  is the exogneous factors time series data (each column is a separate factor, and each row is an observation).
[in] nSize is the number of observations.
[in] nFactors is the number of exognous factors
[in] fBetas   is the weights or loading of the exogneous factors
[in] mean is the ARIMA/SARIMA model's long-run mean/trend (i.e. mu). If missing (i.e. NaN), then it is assumed zero.
[in] sigma  is the standard deviation of the model's residuals/innovations.
[in] nIntegral  is the non-seasonal difference order
[in] phis are the coefficients's values of the non-seasonal AR component
[in] p is the order of the non-seasonal AR component
[in] thetas  are the coefficients's values of the non-seasonal MA component
[in] q is the order of the non-seasonal MA component
[in] nSIntegral  is the seasonal difference
[in] nSPeriod  is the number of observations per one period (e.g. 12=Annual, 4=Quarter)
[in] sPhis  are the coefficients's values of the seasonal AR component
[in] sP is the order of the seasonal AR component
[in] sThetas   are the coefficients's values of the seasonal MA component
[in] sQ is the order of the seasonal MA component
[in] retType   is a switch to select a output type
Order  Description
1  Fitted mean (default)
2  Fitted standard deviation or volatility
3  Raw (non-standardized) residuals
4  Standardized residuals
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
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Examples


   
References
* Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
* Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848