Types of pattern
A few patterns in time series are particularly important.
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Most time series show more than one of these patterns to some degree.
New company registrations in New Zealand
The time series below shows the rate of new company registrations in New Zealand (per 100,000 population) each year from 1960 to 1998.
The dominant feature of this time series is the upward trend over the period.
Tourist arrivals in Fiji
This time series shows the total number of tourists arriving in Fiji each month between January 2009 and December 2012.
Tourist arrivals are highly seasonal. In most tourist destinations, there is a single peak in the summer of each year but in Fiji, the peak is in the winter months of July to September which have less rainfall and are cooler. This is a seasonal pattern.
British Petroleum share prices
This example concerns the price of BP shares in the first 60 full trading days of 2014 — between 2nd January and 19th March. An investor would be interested in using this time series to forecast future changes in the share price.
The 'wavy' appearance indicates autocorrelation — if the share price is high in one day, it is more likely to be high in the adjacent days too.
British Petroleum share volumes traded
The final example shows the number of BP shares that were traded in each of the first 60 full trading days of 2014 — between 2nd January and 19th March.
This time series is dominated by random fluctuations — the volume of shares traded seems to vary unpredictably.