Multi-step ahead forecasting with Weighted Moving Average? The Weighted Moving Average method is usually used for smoothing purposes.
However, it can be used to forecast $Y(t+1)$ based on the last n observed data. 
In real-world problems, forecasting in very short horizon $(h = 1)$ is not very interesting. 
I would like to know if it is possible to use larger forecasting horizons in WMA method like in HoltWinter and ARIMA techniques ?
 A: For an exponentially WMA you would get a flat forecast.  My answer is derived from the information at http://people.duke.edu/~rnau/411avg.htm .  Say you are using an exponentially Weighted Moving average for a series $\{Y_t\}$. You can write it as
$$
S_{t}= \alpha Y_{t}+(1-\alpha)S_{t-1}
$$
Your smoothed value $S_t$ is the point forecast for the next time period, i.e. $\hat Y_{t+1}=S_t$.  Thus
$$
\hat Y_{t+1}= \alpha Y_{t}+(1-\alpha)\hat Y_{t}
$$
The only thing that you can do if you wanted to forecast 2 periods into the future is substitute in $\hat Y_{t+1}$ for $Y_{t+1}$
$$
\hat Y_{t+2}= \alpha \hat Y_{t+1}+(1-\alpha)\hat Y_{t+1}=\hat Y_{t+1}
$$
and so $\hat Y_{t+1}=\hat Y_{t+2}=\hat Y_{t+3}=\hat Y_{t+4}=...$ and so on.
It is not that you can't forecast farther into the future, rather the forecast is flat.  A flat forecast is not necessarily a bad forecast either. The random walk without drift is a flat forecast, and for certain time series, like crude oil for example, it outperforms most all other time series models some of which are very sophisticated.
