Answer: Weighted Moving Average
The pros and cons of moving average forecasting
It is s simple forecasting tool if you are going one time period period into the future.
Ease of use, and not consuming great amounts of time
Very good for short-run forecasts
The moving average is usually used to track the actual data, but it is like a trailer of a truck always behind the data.
The moving average will never reach the highs or lows of the data involved, but it smooths out the data.
Not the best tool in telling us very much about the distant future.
Does not react to variations that may occur such as seasonality.
The pros and cons of weighted moving average
Different weights are put on data from different periods based on spike sales or seasonality.
The most recent data gets the most weight.
This system is built to react more quickly to change (since more recent data is given the most weight).
If the most recent items are given too little weight the forecast can under react to actual change
If the most recent items are given too much weight the forecast might over react to a random fluctuation.
Its a trial and error process that takes some time to find the balance
The pros and cons of Exponential smoothing forecasting
One advantage of the smoothing forecasting methods is their short-term accuracy.
Based in simplicity
It does not require large amount of historical data
Constant monitoring and adjusting the values.
Will not react to changes in these trends it will react only to random fluctuations.
The forecast tend to be lower than the actual demand.
Conclusion based on the pros and cons of three time series forecasting methods
In looking at the data, it seems to have some