Exam 12: Time Series Analysis and Forecasting

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

If the span of a moving average is large - say,12 months - then few observations go into each average,and extreme values have relatively large effect on the forecasts.

Which of the following is not one of the summary measures for forecast errors that is commonly used?

The random walk model is written as: .In this model, represents the:

Perhaps the simplest and one of the most frequently used extrapolation methods is the:

In exponential smoothing models,the forecast is based on the level at time t,L_t,which is not observable and can only be estimated.

If the observations of a time series increase or decrease regularly through time,we say that the time series has a random (or noise)component.

Econometric forecasting models,also called causal models,use regression to forecast a time series variable by using other explanatory time series variables.

Rite Aid pharmacy in Big Rapids,Michigan is using simple exponential smoothing to predict monthly birthday card sales.At the end of October 2004,the pharmacy's forecast for December 2004 sales was 400.In November,420 cards were sold,and during December,425 cards were sold.At the end of December 2004,what is the pharmacy's forecast for the total number of cards that will be sold during March and April of 2005? Use .

In a random walk model,there are significantly more runs than expected,and the autocorrelations are not significant.

Seasonal variations will not be present in a deseasonalized time series.

If we use a value close to 1 for the smoothing constant in a simple exponential smoothing model,then we expect the model to respond very slowly to changes in the level.

The seasonal component of a time series is harder to predict than the cyclic component; the reason is that cyclic variation is much more regular.

(A)Obtain an autocorrelation table for this series. (B)Use the results of (A)to specify one or more "promising" autoregression models.Estimate each model with the available data.Which model provides the best fit to the given data? (C)Use the best autoregression model from (B)to produce a forecast of the CCI in 2007.Also,provide a measure of the likely forecast error. (D)Use the moving average method with a carefully chosen span to forecast this time series in 2007 and 2008.Explain your choice of the span. (E)Between the best autoregression model and the best moving average model,which is best? Explain your answer.

An exponential trend is appropriate when the time series changes by a constant percentage each period.

If a time series exhibits an exponential trend,then a plot of its logarithm should be approximately linear.

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels,trends and cycles in the data.

The moving average method can also be referred to as a (n)_____ method.

Winter's method is an exponential smoothing method,which is appropriate for a series with trend but no seasonality.

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for: