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computer Science


1. This smoothing method is powerful for Visualizing Trends and make the

Trends more visible. A.Trailing Moving average B.Differencing C. Centered moving average D.All the above


2.Moving average can be used for forecasting only in series that lack A.Trend B.Seasonality C.Noise D.Both Seasonality and Trend


3.A simple and popular method for removing a trend and / or a seasonal pattern is A.Moving average B.Exponential smoothing C.Differencing D.All the above


4.This is a popular forecasting method that is flexible, has ease of automation, cheap computation, and good performance. A.Simple Exponential Smoothing B.Centered Moving Average C.Holt's Linear trend model D.Differencing


5.The ets Model that has additive error, No trend, and no seasonality is A.MAN B.AAN C.ANN D.AMM

6.For series that contains both trend and seasonality, this method is used A.Centered moving average B.Simple exponential smoothing C.Holt-winter's exponential smoothing D.All the above


7.For series that contains both trend and seasonality, this method is used A.Centered moving average B.Simple exponential smoothing C.Holt-winter's exponential smoothing D.All the above


8.This is an unconnected node. It might arise when someone signs up for a social network service and does not participate A.Clique B.Edge C.Degree D.Singleton


9.This is a network in which each node is directly connected by an edge to every other node. A.Egocentric B. Singleton C.Clique D.Connected


10. In ets function, If you do not know which model to use, you can do automated model selection by leaving out the model option altogether or by using model = "ZZZ". A.True B.False

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