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Trends
Trends
The regular patterns in Time Series graphs allows us to make predictions about what might happen in the future.
Example:
The following time series plot shows a clear upward trend.
The time series below also shows an upward trend, although this may be better represented by a curve rather than a straight line as the increase in the data values seems to accelerate over time.
Seasonal Patterns
Seasonal Patterns
Example:
These data show a seasonal pattern. The pattern repeats every 12 points.
Unusual Features
Unusual Features
Look for unusual observations, also called outliers/spikes. Outliers can have a disproportionate effect on time series models and produce misleading results. Try to identify the cause of any outliers and correct any data-entry errors or measurement errors. Consider removing data values that are associated with abnormal, one-time events, which are also called special causes.
Example:
The following time series plot shows an outlier that was caused by a data-entry error. A technician accidentally entered the value 4 in the worksheet instead of 40.
Unusual Features - Sudden Shifts
Look for sudden shifts in the series or sudden changes to trends. Try to identify the cause of such changes.
Example:
The following time series plot shows a drastic shift in the cost of a process after 15 months. You should investigate the reason for the shift.
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