| int __stdcall NDK_MAE | ( | double * | X, |
| double * | Y, | ||
| size_t | N, | ||
| double * | retVal | ||
| ) |
Calculates the mean absolute error function for 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 mean absolute error is a common measure of forecast error in time series analysis.
- 2. The time series is homogeneous or equally spaced.
- 3. The two time series must be identical in size.
- 4. The mean absolute error is given by:
- \[\mathrm{MAE}=\frac{\mathrm{SAE}}{N}=\frac{\sum_{i=1}^N \left | x_i - \hat x_i \right |}{N}\],
- where:
- \(\{x_i\}\) is the actual observations time series.
- \(\{\hat x_i\}\) is the estimated or forecasted time series.
- \(\mathrm{SAE}\) is the sum of the absolute errors (or deviations).
- \(N\) is the number of non-missing data points.
- Requirements
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
- Examples
-
| Namespace: | NumXLAPI |
| Class: | SFSDK |
| Scope: | Public |
| Lifetime: | Static |
| int NDK_MAE | ( | double[] | pData1, |
| double[] | pData2, | ||
| UIntPtr | nSize, | ||
| ref double | retVal | ||
| ) |
Calculates the mean absolute error function for 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 mean absolute error is a common measure of forecast error in time series analysis.
- 2. The time series is homogeneous or equally spaced.
- 3. The two time series must be identical in size.
- 4. The mean absolute error is given by:
- \[\mathrm{MAE}=\frac{\mathrm{SAE}}{N}=\frac{\sum_{i=1}^N \left | x_i - \hat x_i \right |}{N}\],
- where:
- \(\{x_i\}\) is the actual observations time series.
- \(\{\hat x_i\}\) is the estimated or forecasted time series.
- \(\mathrm{SAE}\) is the sum of the absolute errors (or deviations).
- \(N\) is the number of non-missing data points.
- Exceptions
-
Exception Type Condition None N/A
- Requirements
-
Namespace NumXLAPI Class SFSDK Scope Public Lifetime Static Package NumXLAPI.DLL
- Examples
-
- References
- Hull, John C.; Options, Futures and Other DerivativesFinancial Times/ Prentice Hall (2011), ISBN 978-0132777421
