Regular and irregular time series

Time series are typically assumed to be generated at regularly spaced interval of time, and so are called regular time series. The data can include a timestamp explicitly or a timestamp can be implied based on the intervals at which the data is created. Time series without an associated timestamp are automatically assumed to be regular time series.

An irregular time series is the opposite of a regular time series. The data in the time series follows a temporal sequence, but the measurements might not happen at a regular time interval. For example, the data might be generated as a burst or with varying time intervals. Account deposits or withdrawals from an ATM are examples of an irregular time series.

All operators in the TimeSeries Toolkit can process regular time series. Some operators, such as GAMScorer, are able to deal with irregular time series. Operators detect and handle irregular time series as follows:
  • If a time stamp is provided and the operator detects that the time series is irregular, the operator generates a warning.
  • If a time stamp is not provided, the operator assumes that the time series is regular. If the time series is irregular, the operator might generate unexpected or non-optimal results.