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.
World rice production
The time series plot below shows the total world rice production each year from 1961 to 2012.
The dominant feature of this time series is the upward trend over the period.
Temperature in Sydney
This time series shows the mean daily maximum temperature in Sydney each month between January 2009 and May 2013.
Temperature is highly seasonal and the seasonal pattern of a summer peak between November and April each year is clear.
Southern Oscillation
The Southern Oscillation Index is defined as the barometric pressure difference between Tahiti and the Darwin Islands at sea level. The southern oscillation is a predictor of El Nino which in turn is thought to be a driver of world-wide weather. Specifically, repeated southern oscillation values less than -1 typically defines an El Nino. The time series plot shows the index between January 2005 and December 2013.
There is a lot of random variation in this time series and an outlier in February 2005. However the index remains low for extended periods in 2006/7 and 2009/10 and is relatively high for most of 2010 and 2011. These periodic highs and lows indicate autocorrelation.
Annual barley yields
The final example shows annual barley yields per acre in England & Wales from 1884 to 1939.
This time series is dominated by random fluctuations — the yields seems to vary unpredictably.