Basic Features

Example:

The regular patterns in Time Series graphs allows us to make predictions about what might happen in the future.

Long Term Trend:

Features of Time Series

1. Look for trends

Movie ticket sales is increasing at a rate of about $200 per week.

Seasonal Variation:

Example:

The following time series plot shows a clear upward trend. There may also be a slight curve in the data, because the increase in the data values seems to accelerate over time.

We can see there is a repeating cycle of clear highs every Saturday/Sunday and lows every Tuesday.

2. Look for Seasonal Patterns

Example:

These data show a seasonal pattern. The pattern repeats every 12 months.

Outliers/Spikes

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.

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.

Sudden shifts

Look for sudden shifts in the series or sudden changes to trends. Try to identify the cause of such changes.

For 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.