The result is one row for each minute within our defined data set. The statement uses the TIMESERIES clause to divide the time range into 1 minute slices. This example shows how to use the TS_FIRST_VALUE function with the constant interpolation scheme. Using the TickStore table presented earlier, let’s apply time series analytics to find some missing data points. Given the two time series aggregate functions, and the two methods of interpolations, you have four options to construct missing data points:ġ.TS_FIRST_VALUE with constant scheme, which fills in missing data points according to the last value seen so far, and returns the value at the beginning of the time slice:Ģ.TS_FIRST_VALUE with linear scheme, which fills in missing data points according to a linear slope, and returns the value at the beginning of the time slice:ģ.TS_LAST_VALUE with constant scheme, which fills in missing data points according to the last value seen so far, and returns the value at the end of the time slice:Ĥ.TS_LAST_VALUE with linear scheme, which fills in missing data points according to a linear slope, and returns the value at the end of the time slice: Using linear interpolation, a constructed data point value falls between the values of the last known data point and the next known data point following the constructed point. It interpolates values in a linear slope based on the specified time slice. Linear Interpolation: This scheme is specified by the LINEAR keyword in both time series aggregate functions.This means that the value at a constructed data point matches the value at the last known data point. The constant interpolation scheme fills in missing data based on the last known value at any given time. It is specified with the CONST keyword in both time series aggregate functions. Ĝonstant Interpolation: This scheme is the default method for gap-filling in Vertica.Vertica provides two interpolation schemes: Interpolation schemes determine how Vertica calculates or “fills in” the missing data points. TS_LAST VALUE: Returns the value at the end of a time slice.Įach aggregate function takes a column and an interpolation scheme, as described in the next section.TS_FIRST_VALUE: Returns the value at the beginning of a time slice.The two time series aggregate functions determine which value within your defined time slice to output: 10:09:00 through 10:11:59 Time Series Aggregate Functions TIMESERIES slice_time AS '3 minutes'… The result consists of four, 3-minute time slices: Slice_time is a time column produced by the clause that stores the time slices generated from gap filling: Provide the TIMESERIES clause an INTERVAL slice time that determines at which intervals output records are produced. ![]() You can use time series analytics to estimate the bid price at the times that fall between these known times, such as 10:02:00. There are time lags between several bids. This post refers to the TickStore table, which contains stock bid prices over time for company XYZ: Gap-filling, which has interpolation schemes that construct new data points within the range of a discrete set of known data points.Time series aggregate functions, which determine whether to return the first or last value in a given time slice. ![]()
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