Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. The table shows the units sold for 18 weeks. The analyst uses the Moving Average model to forecast the sales. MAD for forecasts shows the deviation of forecasted demand from actual demand. There are two weeks, three weeks, and four weeks on average. As the period increases, the 18-week absolute error increases. A weighted average is sometimes more accurate than a simple average. In a weighted average, each data point value is multiplied by the assigned weight, which is then summed and divided by the number of data points. A weighted average can improve the data's accuracy. The best Weighted Average Model is a two-week period because it has less absolute error and is more accurate. MSE stands for Mean Squared Error. MSE is calculated by squaring the residual errors of each data point, summing the squared errors, and dividing the sum by the total number of data points. MAPE stands for Mean Absolute Percentage Error. MAPE is a metric that defines the accuracy of a forecasting method. It represents the average of the absolute percentage errors of each entry in a dataset to calculate how accurate the forecasted quantities were compared to the actual quantities. Exponential smoothing is a broadly accurate forecasting method for short-term forecasts. The technique assigns larger weights to more recent observations while assigning exponentially decreasing weights as the observations get increasingly distant. It is most effective when the time series values follow a gradual trend and display seasonal behavior in which the values follow a repeated cyclical pattern over a given number of time steps. The exponential smoothing is .1 to .9. As the smoothing increases, the MAPE percentage decrease