# NDK_ARIMA_PARAM

 int __stdcall NDK_ARIMA_PARAM ( double * pData, size_t nSize, double * mean, double * sigma, WORD nIntegral, double * phis, size_t p, double * thetas, size_t q, MODEL_RETVAL_FUNC retType, size_t maxIter )

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] pData  is the univariate time series data (a one dimensional array).
[in] nSize  is the number of observations in pData.
[in,out] mean  is the ARMA model mean (i.e. mu).
[in,out] sigma  is the standard deviation of the model's residuals/innovations.
[in] nIntegral  is the model's integration order.
[in,out] 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,out] 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] retType  is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors
Order   Description
1 Quick guess (non-optimal) of parameters values (default)
2 Calibrated (optimal) values for the model's parameters
3 Standard error of the parameters' values
[in] maxIter  is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
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. ARIMA_PARAM returns an array for the values (or errors) of the model's parameters in the following order:
• $$\mu$$
• $$\phi_1,\phi_2,...,\phi_p$$
• $$\theta_1,\theta_2,...,\theta_q$$
• $$\sigma$$
5. The integration order argument (d) must be a positive integer.
6. The long-run mean can take any value or may 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.
• 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_ARIMA_PARAM ( double[] pData, UInPtr nSize, ref double mean, ref double sigma, short nIntegral, double[] phis, UIntPtr p, double[] thetas, UIntPtr q, MODEL_RETVAL_FUNC retType, UIntPtr maxIter )

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

Return Value

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

 NDK_SUCCESS operation successful Error Error Code
Parameters
[in] pData  is the univariate time series data (a one dimensional array).
[in] nSize  is the number of observations in pData.
[in,out] mean  is the ARMA model mean (i.e. mu).
[in,out] sigma  is the standard deviation of the model's residuals/innovations.
[in] nIntegral  is the model's integration order.
[in,out] 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,out] 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] retType  is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors
Order   Description
1 Quick guess (non-optimal) of parameters values (default)
2 Calibrated (optimal) values for the model's parameters
3 Standard error of the parameters' values
[in] maxIter  is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
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. ARIMA_PARAM returns an array for the values (or errors) of the model's parameters in the following order:
• $$\mu$$
• $$\phi_1,\phi_2,...,\phi_p$$
• $$\theta_1,\theta_2,...,\theta_q$$
• $$\sigma$$
5. The integration order argument (d) must be a positive integer.
6. The long-run mean can take any value or may 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.
• 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