Question 2 Not completeS, and S, were, respectively, 0.9860,0.9504, 1.0597 and 1

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Question 2 Not completeS, and S, were, respectively, 0.9860,0.9504, 1.0597 and 1.0037. The adjusted values were, respectively, 0.9861, Marked out of 1 In the Pre Class Video "Time Series Forecasting Measuring Seasonality" Slide 7, the unadjusted values of S,S 0.9505, 1.0600 and 1.0038. In a different example, if the unadjusted values of S, S, S, and S, were, respectively, 0.9762,0.9541, 1.0477 and F Flag question 1.0137, calculate and provide the adjusted value for S Answer: Check Which one of the following statements (a, b, c, d) is FALSE? Question 3 Incorrect Marked out of 1 Select one: a. Forecast error is the difference between the actual value and the forecast value. For MAD and MSFE, it is very important to remember that Et = Yt-Ft. not Et-Ft-Y. b. One way of deciding which forecasting method to select is to compare the forecast accuracy of different methods. Flag question . c. Compared with MAD, MSFE weights large errors more heavily. X d. MAD weights all errors equally

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5) a and b are false:

Clearly, as the original time series is a sum of its constituents, each component has the same dimension or units as the original time series. In the multiplicative model, only the trend has the same dimensions (or units) as the raw series. The remaining components are dimensionless factors centered on one.

c : True

Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components. Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting.

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