int __stdcall NDK_SAD ( double * X, double * Y, size_t N, double * retVal )

Calculates the sum of absolute errors (SAE) between the forecast and the eventual outcomes.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] X is the original (eventual outcomes) time series sample data (a one dimensional array). [in] Y is the forecast time series data (a one dimensional array). [in] N is the number of observations in X. [out] retVal is the calculated value of this function.
Remarks
1. The time series is homogeneous or equally spaced.
2. The two time series must be identical in size.
3. A missing value (say $$x_k$$ or $$\hat x_k$$) in either time series will exclude the data point $$(x_k,\hat x_k)$$  from the SSE.
4. The sum of absolute errors (SAE) or deviations (SAD), is defined as follows:
$\mathrm{SAE}=\mathrm{SAD}=\sum_{i=1}^N \left | x_i-\hat x_i \right |$,
where:
• $$\{x_i\}$$ is the actual observations time series.
• $$\{\hat x_i\}$$ is the estimated or forecasted time series.
Requirements
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_SAD ( double[] pData1, double[] pData2, UIntPtr nSize, ref double retVal )

Calculates the sum of absolute errors (SAE) between the forecast and the eventual outcomes.

Return Value

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

 NDK_SUCCESS operation successful Error Error Code
Parameters
 [in] pData1 is the original (eventual outcomes) time series sample data (a one dimensional array). [in] pData2 is the forecast time series data (a one dimensional array). [in] nSize is the number of observations in pData1. [out] retVal is the calculated value of this function.
Remarks
1. The time series is homogeneous or equally spaced.
2. The two time series must be identical in size.
3. A missing value (say $$x_k$$ or $$\hat x_k$$) in either time series will exclude the data point $$(x_k,\hat x_k)$$  from the SSE.
4. The sum of absolute errors (SAE) or deviations (SAD), is defined as follows:
$\mathrm{SAE}=\mathrm{SAD}=\sum_{i=1}^N \left | x_i-\hat x_i \right |$,
where:
• $$\{x_i\}$$ is the actual observations time series.
• $$\{\hat x_i\}$$ is the estimated or forecasted time series.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
Hull, John C.; Options, Futures and Other DerivativesFinancial Times/ Prentice Hall (2011), ISBN 978-0132777421