| 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
-
- The underlying model is described here.
- NDK_ARMA_SIM returns an array of one simulation path starting from the end of the input data.
- The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. NaN) at either end.
- The long-run mean can take any value or be omitted, in which case a zero value is assumed.
- The residuals/innovations standard deviation (sigma) must be greater than zero.
- 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).
- 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
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
- 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
-
- The underlying model is described here.
- NDK_ARMA_SIM returns an array of one simulation path starting from the end of the input data.
- The input data argument (i.e. latest observations) is optional. If omitted, an array of zeroes is assumed.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. NaN) at either end.
- The long-run mean can take any value or be omitted, in which case a zero value is assumed.
- The residuals/innovations standard deviation (sigma) must be greater than zero.
- 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).
- 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 Class SFSDK Scope Public Lifetime Static Package 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
