| int __stdcall NDK_SESMTH | ( | double * | pData, |
| size_t | nSize, | ||
| BOOL | bAscending, | ||
| double * | alpha, | ||
| int | nHorizon, | ||
| BOOL | bOptimize, | ||
| double * | retVal | ||
| ) |
Returns the (Brown's) simple exponential (EMA) smoothing estimate of the value of pData at time t+m (based on the raw data up to time t).
- 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 elements in pData. [in] bAscending is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)). [in] alpha is the smoothing factor (alpha should be between zero and one (exclusive)). If missing or omitted, a value of 0.333 is used. [in] nHorizon is the forecast time horizon beyond the end of pData. If missing, a default value of 0 (latest or end of pData) is assumed. [in] bOptimize is a flag (True/False) for searching and using the optimal value of the smoothing factor. If missing or omitted, optimize is assumed false. [out] retVal is the calculated value of this function.
- Remarks
-
- The simple exponential smoothing function \(S_t\) is defined as follow: \[S_1=X_1 \] \[S_{t \succ 1}= \alpha\times X_{t-1} + (1-\alpha)\times S_{t-1} \] \[S_{t \succ T}=S_T\] Where:
- \(X_t\) is the value of the time series at time t.
- \(T\) is the time of the latest observation in the sample data.
- \(\alpha\) is the smoothing factor.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. NaN) at either end.
- Exponential smoothing and moving average are similar in that they both assume a stationary, not trending, time series.
- If a trend exists in the time series, the simple exponential lags behind the trend.
- To search for the optimal values of the smoothing factor (alpha), the time series must have two(2) or more non-missing observations.
- The exponential smoothing function differs from the weighted moving average (WMA) in that exponential smoothing takes into account all past data, whereas moving average only takes into account k past data points.
- The simple exponential smoothing function \(S_t\) is defined as follow: \[S_1=X_1 \] \[S_{t \succ 1}= \alpha\times X_{t-1} + (1-\alpha)\times S_{t-1} \] \[S_{t \succ T}=S_T\] Where:
- Requirements
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
- Examples
-
| Namespace: | NumXLAPI |
| Class: | SFSDK |
| Scope: | Public |
| Lifetime: | Static |
| int NDK_SESMTH | ( | double[] | pData, |
| int | nSize, | ||
| BOOL | bAscending, | ||
| ref double | alpha, | ||
| int | nHorizon, | ||
| BOOL | bOptimize, | ||
| ref double | retVal | ||
| ) |
Returns the (Brown's) simple exponential (EMA) smoothing estimate of the value of X at time t+m (based on the raw data up to time t).
- 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 elements in pData. [in] bAscending is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)). [in] alpha is the smoothing factor (alpha should be between zero and one (exclusive)). If missing or omitted, a value of 0.333 is used. [in] nHorizon is the forecast time horizon beyond the end of pdata. If missing, a default value of 0 (latest or end of pdata) is assumed. [in] bOptimize is a flag (True/False) for searching and using the optimal value of the smoothing factor. If missing or omitted, optimize is assumed false. [out] retVal is the calculated value of this function.
- Remarks
-
- The simple exponential smoothing function \(S_t\) is defined as follow: \[S_1=X_1 \] \[S_{t \succ 1}= \alpha\times X_{t-1} + (1-\alpha)\times S_{t-1} \] \[S_{t \succ T}=S_T\] Where:
- \(X_t\) is the value of the time series at time t.
- \(T\) is the time of the latest observation in the sample data.
- \(\alpha\) is the smoothing factor.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. NaN) at either end.
- Exponential smoothing and moving average are similar in that they both assume a stationary, not trending, time series.
- If a trend exists in the time series, the simple exponential lags behind the trend.
- To search for the optimal values of the smoothing factor (alpha), the time series must have two(2) or more non-missing observations.
- The exponential smoothing function differs from the weighted moving average (WMA) in that exponential smoothing takes into account all past data, whereas moving average only takes into account k past data points.
- The simple exponential smoothing function \(S_t\) is defined as follow: \[S_1=X_1 \] \[S_{t \succ 1}= \alpha\times X_{t-1} + (1-\alpha)\times S_{t-1} \] \[S_{t \succ T}=S_T\] Where:
- 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
