Exam 12: Time Series Analysis and Forecasting

The time series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called cyclical.

The purpose of using the moving average is to take away the short-term seasonal and random variation,leaving behind a combined trend and cyclical movement.

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.

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.

The most common form of autocorrelation is positive autocorrelation,in which:

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

The runs test uses a series of 0's and 1's.The 0's and 1's represent whether each observation is:

The components of a time series include:

A meandering pattern is an example of a random time series.

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

The time series component that reflects a long-term,relatively smooth pattern or direction exhibited by a time series over a long time period,is called seasonal.

A shortcoming of the RMSE (root mean square error)is that it is not in the same units as the forecast variable.

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.

If a random series has too few runs,then it is zigzagging too often.

The following are the values of a time series for the first four time periods: t 1 2 3 4 24 25 26 27 Using a four-period moving average,the forecasted value for time period 5 is: