I sent the first question for information. The second question is what I need.

techniques on a historical data that they have in terms of the forecasting accuracy on a certain product's weekly sales data. They have recorded the sales values in the last 13 weeks and you can see the corresponding value of each week as a time series data in the below table. Period Actual 1 141 2 104 3 145 4 147 5 150 6 150 7 132 8 136 9 108 10 103 11 113 12 141 13 121 Please use this historical data to perform the following actions: a) Plot the time series as a scatter plot (5 pts) b) Make forecasts starting from the 9 period for five periods using moving average method (you can choose n=5; that is the number of historical data points you should use in calculation) (15 pts) c) Make forecasts starting from the period for five periods using weighted moving average method (you can use the following weight scheme w = 0.3, w = 0.2,w.= 0.2, w.-0.15, w = 0.15) (15 pts) d) Make forecasts starting from the 9 period for five periods using exponential smoothing method (you can use smoothing factor as 0.25). (15 p 5 pts) e) Calculate the accuracy metrics we covered in class for all the methods you used above. Make a comparison between the techniques. (15 pts) 2. Company B have made a recent study on their historical data of the unit order price of a certain product. Based on this monthly time series they have discovered two behaviors regarding the factor(i.e. price) they want to forecast. The first behavior was a positive trend in the values. They were able to fit a linear trend line to the available data. However they have realized that it was not enough to account for all the fluctuations. The following investigation revealed that the price values were also fluctuating based on seasonal effects. Eventually they were able to build a forecasting system to predict the price of this certain product. All the trend line components and seasonality indices are provided below. Please make predictions for the next ten periods using the derived forecasting system. You may assume the first period you start forecasting is associated with season 1. (Generating trend based portion is 20 pts; Correctly adding seasonal factors to it is 15 pts) Basic Slope = 7,5 Intercept 57 Number of seasons = 4 Season 1 2 3 4 Multiplicative Index 1,3 1,02 0,75 0,85 HINT: You can use the excel template that we used during the class and saved inside class material folder of Teams group for the BA (901)