Analysis of Time Series. For AS90641 Part 2 Extra for Experts. Contents. This resource is designed to suggest some ways students could meet the requirements of AS 90641. It shows some common practices in New Zealand schools and suggests other simplified statistical methods.
Analysis of Time Series
Extra for Experts
Created by Polly Stuart
Look at : the trend
the seasonal component
the irregular component
Indexes show how prices have changed over time. They show the percentage increase in prices since a base period. The index for the base period is usually 1000.
An index of 1150 shows that prices have increased 15 percent since the base period.
You can use indexes to ‘deflate’ time series data which contains dollar values.
Statistics New Zealand indexes include:
Consumers Price IndexLabour Cost Index
Food Price IndexFarm Expenses Price Index
Look at the CPI data. Which is the base period? How do you know?
If the value of sales from clothing shops are increasing over time there several possible reasons:
To help find out if total sales are increasing because of inflation we can turn the sales into constant 1999 dollars using the value of the CPI for each year.
The present base period for the Consumers Price Index (CPI) is 1999.
Assume that the CPI now is 1150.
In 1999, $100 could buy the same amount as:
can buy now
Now, $100 can buy the same amount as:
could buy in 1999
Calculate your deflated value
We will use constant 1999 dollars for the rest of this exercise.
Use this formula to calculate the value in constant 1999 dollars.
Some data follows an additive model where:
Data value = trend + seasonal + irregular
Other data follows a multiplicative model where:
Data value = trend x seasonal x irregular
When the size of the seasonal component stays about the same as the trend changes, then an additive method is usually best.
When the size of the seasonal component increases as the trend increases, then a multiplicative method may be better.
Look again at the graph below
In the previous PowerPoint we used an additive model and we will do this also for this data
(An example of using a multiplicative model is given at the end of the third presentation).
Your spreadsheet should look like this:
Describe what you can see.
Does this linear trend model look sensible?
But is it realistic?
Describe the patterns you can see.
You can also identify amounts easily from the graph.
Subtract the values in the ‘Seasonal’ column from the ‘Seasonal and Irregular’ column. A graph is often useful.
Highlight the date and irregular columns for the graph.
Both the pattern of the irregular component and any extreme values are worth commenting on.
This is not the end!
Continue the analysis and write a report on retail clothing sales.
Some ideas are given in the next presentation, Reporting.