There are at least three forecasting methods. Name them and choose one to discuss in more detail, including advantages and disadvantages

Answer 1

Simple Moving Average
The simple moving average is probably the simplest to develop method in basic time series forecasting. It makes forecasts based on recent demand history and allows for the removal of random effects. The simple moving average method does not accommodate seasonal, trend, or business cycle influences. This method simply averages a predetermined number of periods and uses this average as the demand for the next period. Each time the average is computed, the oldest demand is dropped and the most recent demand is included. A weakness of this method is that it forgets the past quickly. A strength is that it is quick and easy to use.

Weighted Moving Average
In the simple moving average method, each previous demand period was given an equal weight. The weighted moving average method assigns a weight to each previous period with higher weights usually given to more recent demand. The weights must be equal to one. The weighted moving average method allows emphasis to be placed on more recent demand as a predictor of future demand. However, the results from the weighted moving average method are still not very good forecasts of demand. There are three possible causes for this. First, the weights assigned to the previous periods might not accurately reflect the patterns in demand. Second, the number of periods used to develop the forecast might not be the appropriate number. Finally, the weighted moving average technique does not easily accommodate demand patterns with seasonal influences.

Exponential Smoothing
Exponential smoothing is one of the most commonly used techniques because of its simplicity and its limited requirements for data. Exponential smoothing needs three types of data: (1) an average of previous demand, (2) the most recent demand, and (3) a smoothing constant. The smoothing constant must be between 0 and 1 . Using a higher constant assumes that the most recent demand is a better predictor of future demand. Exponential smoothing forecasts will lag actual demand. If demand is relatively constant, exponential smoothing will produce a relatively accurate forecast. However, highly seasonal demand patterns or patterns with trends can cause inaccurate forecasts using exponential smoothing.