NDK_SARIMAX_SIM

Last Modified on 03/14/2016 1:29 pm CDT
int __stdcall NDK_SARIMAX_SIM ( double *  fBetas,
size_t  nFactors,
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,
double *  pData,
double **  pFactors,
size_t  nSize,
UINT  nSeed,
size_t  nStep,
double *  retVal 
)

Returns an array of cells for the initial (non-optimal), optimal or standard errors of the model's parameters.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in,out] fBetas is the weights or loading of the exogneous factors
[in] nFactors is the number of exognous factors
[in,out] mean is the mean of the ARMA process
[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] pData is the univariate time series data (a one dimensional array).
[in] pFactors is the past 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 X.
[in] nSeed is an unsigned integer for setting up the random number generators
[in] nStep is the simulation time/horizon (expressed in terms of steps beyond end of the time series).
[out] retVal is the simulated 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.
  4. Each column in the explanatory factors input matrix (i.e. X) corresponds to a separate variable.
  5. Each row in the explanatory factors input matrix (i.e. X) corresponds to an observation.
  6. Observations (i.e. rows) with missing values in X or Y are assumed missing.
  7. The number of rows of the explanatory variable (X) must be greater or equal to the number of rows of the response variable (Y) plus simulation horizon.
  8. The intercept or the regression constant term input argument is optional. If omitted, a zero value is assumed.
  9. For the input argument - Beta:
    • The input argument is optional and can be omitted, in which case no regression component is included (i.e. plain SARIMA).
    • The order of the parameters defines how the exogenous factor input arguments are passed.
    • One or more parameters may have missing value or an error code(i.e. #NUM!, #VALUE!, etc.).
  10. The long-run mean argument (mean) of the differenced regression residuals can take any value. If omitted, a zero value is assumed.
  11. The residuals/innovations standard deviation (sigma) must greater than zero.
  12. For the input argument - phi (parameters of the non-seasonal AR component):
    • The input argument is optional and can be omitted, in which case no non-seasonal AR component is included.
    • The order of the parameters starts with the lowest lag
    • The order of the non-seasonal AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing, or error).
  13. For the input argument - theta (parameters of the non-seasonal MA component):
    • The input argument is optional and can be omitted, in which case no non-seasonal MA component is included.
    • The order of the parameters starts with the lowest lag
    • The order of the non-seasonal MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing, or error).
  14. For the input argument - sPhi (parameters of the seasonal AR component):
    • The input argument is optional and can be omitted, in which case no seasonal AR component is included.
    • The order of the parameters starts with the lowest lag
    • The order of the seasonal AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing, or error).
  15. For the input argument - sTheta (parameters of the seasonal MA component):
    • The input argument is optional and can be omitted, in which case no seasonal MA component is included.
    • The order of the parameters starts with the lowest lag
    • The order of the seasonal MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing, or error).
  16. The non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed zero.
  17. The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed zero.
  18. The season length - s - is optional and can be omitted, in which case s is assumed zero (i.e. Plain ARIMA).
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

See Also

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