# NDK_ARMA_SIM

 int __stdcall NDK_ARMA_SIM ( double mean, double sigma, double * phis, size_t p, double * thetas, size_t q, double * pData, size_t nSize, UINT nSeed, double * retArray, size_t nSteps )

Returns an array of cells for the simulated values.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] mean is the ARMA model mean (i.e. mu). [in] sigma is the standard deviation of the model's residuals/innovations. [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] pData are the values of the latest (most recent) observations [in] nSize is the number elements in pData [in] nSeed is a number to initialize the psuedorandom number generator. [out] retArray is the output array to hold nSteps future simulations [in] nSteps is the number of future steps to simulate for.
Remarks
1. The underlying model is described here.
2. NDK_ARMA_SIM returns an array of one simulation path starting from the end of the input data.
3. The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
4. The time series is homogeneous or equally spaced.
5. The time series may include missing values (e.g. NaN) at either end.
6. The long-run mean can take any value or be omitted, in which case a zero value is assumed.
7. The residuals/innovations standard deviation (sigma) must be greater than zero.
8. For the input argument - phi:
• The input argument is optional and can be omitted, in which case no AR component is included.
• The order of the parameters starts with the lowest lag.
• The order of the AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
9. For the input argument - theta:
• The input argument is optional and can be omitted, in which case no MA component is included.
• The order of the parameters starts with the lowest lag.
• One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
• The order of the MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
Requirements
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_ARMA_SIM ( double mean, double sigma, double[] phis, UIntPtr p, double[] thetas, UIntPtr q, double[] pData, UIntPtr nSize, int Seed, double[] retArray, UIntPtr nSteps )

Returns an array of cells for the simulated values.

Return Value

a value from NDK_RETCODE enumeration for the status of the call.

 NDK_SUCCESS operation successful Error Error Code
Parameters
 [in] mean is the ARMA model mean (i.e. mu). [in] sigma is the standard deviation of the model's residuals/innovations. [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] pData are the values of the latest (most recent) observations [in] nSize is the number elements in pData [in] nSeed is a number to initialize the psuedorandom number generator. [out] retArray is the output array to hold nSteps future simulations [in] nSteps is the number of future steps to simulate for.
Remarks
1. The underlying model is described here.
2. NDK_ARMA_SIM returns an array of one simulation path starting from the end of the input data.
3. The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
4. The time series is homogeneous or equally spaced.
5. The time series may include missing values (e.g. NaN) at either end.
6. The long-run mean can take any value or be omitted, in which case a zero value is assumed.
7. The residuals/innovations standard deviation (sigma) must be greater than zero.
8. For the input argument - phi:
• The input argument is optional and can be omitted, in which case no AR component is included.
• The order of the parameters starts with the lowest lag.
• The order of the AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
9. For the input argument - theta:
• The input argument is optional and can be omitted, in which case no MA component is included.
• The order of the parameters starts with the lowest lag.
• One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
• The order of the MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
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