Returns the exponentially-weighted moving (rolling/running) average using the previous N data points.

## Syntax

**NxEMA**(

**X, Order, N, Variant, Return**)

- X
- is the univariate time series data (a one-dimensional array of cells (e.g. rows or columns)).
- Order
- is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0))
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date) - N
- is the smoothing period expressed in number of data points (e.g. days).
- Variant
- is the variant/type of the exponentially-weighted moving average (i.e. 0= Simple (default), 1= Double, 2=Triple, 3=Zero-lagged). If missing or omitted, a simple exponentially-weighted moving average (i.e. Variant = 0) is assumed.
Value Description 0 Plain/simple exponentially-weighted moving average (EMA) 1 Double exponentially-weighted moving average (D-EMA) 2 Triple exponentially-weighted moving average (T-EMA) 3 Zero-lagged exponential-weighted moving average (ZLEMA) - Return
- is return type of the function: 0 = last/most recent value (default), 1 = filtered time series (array.
Value Description 0 Return smoothed value of the Last/most-recent observation 1 Return the whole smoothed time series (array)

## Remarks

- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- The formula of exponential moving average definition is expressed in technical analysis terms as follow: $$ \textrm{EMA}_t = \textrm{EMA}_{t-1} + \alpha \times ( x_t - \textrm{EMA}_{t-1}) $$ Where:
- $\textrm{EMA}_t$ is the exponential moving average at time t.
- $x_t$ is the value of the time series at time t.
- $\alpha$ is the smoothing factor (between 0 and 1) represents the degree of weighting decrease. For EMA, the $\alpha$ is exp[ressed as follow: $$ \alpha = \frac{2}{N+2} $$ Where:
- $N$ is the smoothing period expressed in number of data points (e.g. days)

- The exponential moving average with a smooth period of N data points is expressed by N-period EMA.

- The double exponential moving average (aka. DEMA or D-EMA) is expressed as follow: $$\textrm{D-EMA}_t = 2 \times \textrm{EMA}_t -\textrm{EMA}(\textrm{EMA})_t$$ Where:
- $\textrm{EMA}(\textrm{EMA})$ is the exponential moving average of the exponential moving average.

- The triple exponential moving average (aka. TEMA or T-EMA) is expressed as follow: $$\textrm{T-EMA} = 3\times \textrm{EMA}_t -3\times\textrm{EMA}(\textrm{EMA})_t + \textrm{EMA}(\textrm{EMA}(\textrm{EMA}))_t$$
- For zero-lag exponential moving average (ZLEMA), $$ k = (N-1)/2 $$ $$ Y_t = x_t + (x_t - x_{t-k})$$ $$ \textrm{ZLEMA}_t = \textrm{EMA}_t^N (Y_t) $$ Where:
- $k$ is the number of periods used to remove cumulative effect of moving average.
- $Y_t$ is the de-lagged data. Data is de-lagged by removing the data from k-periods ago, thus removing the cumulative effect of the moving average.

- In the case the smoothing period ($N$) is an even number, then $k$ and $Y_t$ are calculated as follow: $$ k = \frac{N}{2}$$ $$ Y_t = 2x_t - \left(\frac{x_{t-k}+x_{t-k-1}}{2}\right)$$
- The ZLEMA technical indicator was created by John Ehlers and Ric Way.
- The NxEMA function is available starting with version 1.66 PARSON.

## Files Examples

## Related Links

## References

- R.J. Hyndman, A.B. Koehler, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22 (2006), pp. 679-688
- James Douglas Hamilton; Time Series Analysis; Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896
- Tsay, Ruey S.; Analysis of Financial Time Series; John Wiley & SONS; 2nd edition(Aug 30, 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

## Comments

Please sign in to leave a comment.