Analyzing time series data

A time series is a sequence of data values measured at successive, though not necessarily regular, points in time. You can use special SQL functions to process time series data, for example, to identify trends and to predict future values based on these trends.

Examples of time series are:
  • Stock share prices and trading volumes
  • Electrocardiogram (ECG) data
  • Temperature or seismographic data
  • Network performance measurements
  • Electricity usage as recorded by a smart meter and reported via an Internet of Things data feed
  • A clickstream log that records, for an individual computer user, which buttons were clicked, which pages or websites were visited, and so on

Each entry in a time series is called an observation. Each observation comprises a timetick (a 64-bit integer that indicates when the observation was made) and the data recorded for that observation. The recorded data can be either numerical (for example, temperature or stock share price) or categorical (for example, geographic area). A time series can be, but must not necessarily be, associated with a time reference system (TRS), which defines the granularity of each timetick and the start time.

You can use a set of integrated SQL functions and data types to process time series data.

You can access and use this Time Series notebook to help you get started and to give you a reference on how to integrate the Time Series functions with Db2 Event Store.

The functions are of the following types:
Artifact creation
Create time series, TRSs, and other artifacts that are used during the processing of time series data.
Exploding and flattening
Convert time series data to tabular data.
Summary
Retrieve information about a time series.
Statistical
Derive statistical insight from time series data. Each statistical function is of one of the following types:
  • A transform produces, as output, a new time series.
  • A reducer produces, as output, a single value per time series or segment, such as a distance or average.
Forecasting
Use forecasting models to detect anomalies or predict future behavior based on past events.
Filtering
Return the subset of a time series that satisfies the filter criteria.
Cleaning
Remove null values from time series.
Segmentation
Create, as output, a segmented version of a time series.
Temporal join and align
Match two time series that have different timestamps (for example, because they have different periodicity) so that they both have the same timestamps. A temporal join produces a single array time series based on the timestamps and values of the two input time series. A temporal align produces two output time series with identical timestamps. In both cases, an interpolator is used to fill in missing values.
Interpolation
Use interpolation either to fill missing values or to resample a time series at a different periodicity.
String matching
Match the values of a string time series against a string sequence (sequence matching) or a set of strings (item set matching).
Time functions
Convert a string or timestamp into a value of type Long