Time series analytics
Time series analytics evaluate the values of a given set of variables over time and group those values into a window (based on a time interval) for analysis and aggregation. Common scenarios for using time series analytics include: stock market trades and portfolio performance changes over time, and charting trend lines over data.
Since both time and the state of data within a time series are continuous, it can be challenging to evaluate SQL queries over time. Input records often occur at nonuniform intervals, which can create gaps. To solve this problem Vertica provides:

Gapfilling functionality, which fills in missing data points

Interpolation scheme, which constructs new data points within the range of a discrete set of known data points.
Vertica interpolates the nontime series columns in the data (such as analytic function results computed over time slices) and adds the missing data points to the output. This section describes gap filling and interpolation in detail.
You can use eventbased windows to break time series data into windows that border on significant events within the data. This is especially relevant in financial data, where analysis might focus on specific events as triggers to other activity.
Sessionization is a special case of eventbased windows that is frequently used to analyze click streams, such as identifying web browsing sessions from recorded web clicks.
Vertica provides additional support for time series analytics with the following SQL extensions:

TIMESERIES clause in a SELECT statement supports gapfilling and interpolation (GFI) computation.

TS_FIRST_VALUE and TS_LAST_VALUE are time series aggregate functions that return the value at the start or end of a time slice, respectively, which is determined by the interpolation scheme.

TIME_SLICE is a (SQL extension) date/time function that aggregates data by different fixedtime intervals and returns a roundedup input TIMESTAMP value to a value that corresponds with the start or end of the time slice interval.