NDK_SARIMA_FITTED

Last Modified on 07/08/2016 12:22 pm CDT
int NDK_SARIMA_FITTED ( double *  pData,
size_t  nSize,
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] nSize is the number of observations in pData.
[in] mean is the model mean (i.e. mu).
[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.
  4. The long-run mean argument (mean) can take any value or be omitted, in which case a zero value is assumed.
  5. The residuals/innovations standard deviation (sigma) must be greater than zero.
  6. 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).
  7. 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).
  8. 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.
    • One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).
    • 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).
  9. 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.
    • One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
    • 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).
  10. The non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed to be zero.
  11. The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed to be zero.
  12. The season length - s - is optional and can be omitted, in which case s is assumed to be zero (i.e. plain ARIMA).
  13. The function was added in version 1.63 SHAMROCK.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Examples
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_SARIMA_FITTED ( double[]  pData,
UIntPtr  nSize,
double  mean,
double  sigma,
short  nIntegral,
double[]  phis,
UIntPtr  p,
double[]  thetas,
UIntPtr  q,
short  nSIntegral,
double[]  sPhis,
UIntPtr  sP,
double[]  sThetas,
UIntPtr  sQ,
FIT_RETVAL_FUNC  retType 
)

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

Return Value

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

NDK_SUCCESS  operation successful
Error  Error Code
Parameters
[in,out] pData is the univariate time series data (a one dimensional array).
[in] nSize is the number of observations in pData.
[in] mean is the model mean (i.e. mu).
[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.
  4. The long-run mean argument (mean) can take any value or be omitted, in which case a zero value is assumed.
  5. The residuals/innovations standard deviation (sigma) must be greater than zero.
  6. 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).
  7. 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).
  8. 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.
    • One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).
    • 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).
  9. 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.
    • One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
    • 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).
  10. The non-seasonal integration order - d - is optional and can be omitted, in which case d is assumed to be zero.
  11. The seasonal integration order - sD - is optional and can be omitted, in which case sD is assumed to be zero.
  12. The season length - s - is optional and can be omitted, in which case s is assumed to be zero (i.e. plain ARIMA).
  13. The function was added in version 1.63 SHAMROCK.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI
Class SFSDK
Scope Public
Lifetime Static
Package NumXLAPI.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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