# NDK_AIRLINE_FITTED

 int __stdcall NDK_AIRLINE_FITTED ( double * pData, size_t nSize, double mean, double sigma, WORD S, double theta, double theta2, 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] 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] S is the length of seasonality (expressed in terms of lags, where s > 1).
[in] theta is the coefficient of first-lagged innovation (see model description).
[in] theta2 is the coefficient of s-lagged innovation (see model description).
[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 value of the residuals/innovations standard deviation (sigma) must be positive.
6. The season length must be greater than one.
7. The input argument for the non-seasonal MA parameter - theta - is optional and can be omitted, in which case no non-seasonal MA component is included.
8. The input argument for the seasonal MA parameter - theta2 - is optional and can be omitted, in which case no seasonal MA component is included.
Requirements
Header SFSDK.H SFSDK.LIB SFSDK.DLL
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 NDK_AIRLINE_FITTED ( double[] pData, UIntPtr nSize, double mean, double sigma, short dSeason, double theta, double theta2, 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] 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] dSeason is the length of seasonality (expressed in terms of lags, where s > 1).
[in] theta is the coefficient of first-lagged innovation (see model description).
[in] theta2 is the coefficient of s-lagged innovation (see model description).
[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 value of the residuals/innovations standard deviation (sigma) must be positive.
6. The season length must be greater than one.
7. The input argument for the non-seasonal MA parameter - theta - is optional and can be omitted, in which case no non-seasonal MA component is included.
8. The input argument for the seasonal MA parameter - theta2 - is optional and can be omitted, in which case no seasonal MA component is included.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static 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