TimescaleDB Toolkit API reference

Analyze anything you have stored as time-series data, including IoT devices, IT systems, marketing analytics, user behavior, financial metrics, and cryptocurrency.

Approximate count distinct

Estimate the number of distinct values in a dataset, also known as cardinality estimation

Statistical and regression analysis

Functions for statistical analysis and linear regression on time-series data

Minimum and maximum

Find the smallest and largest values in a dataset

Financial analysis

Perform analysis of financial asset data

Percentile approximation

Estimate percentile values and percentile ranks using memory-efficient approximation algorithms

Counters and gauges

Functions for analyzing monotonic counters and gauge metrics

Time-weighted calculations

Calculate time-weighted summary statistics for unevenly sampled data

Downsampling

Functions for downsampling time-series data to visualize trends while preserving visual similarity

Timevector

Functions for working with time-series data as ordered sequences of time-value pairs

Frequency analysis

Functions for analyzing the frequency of values in time-series data

State tracking

Functions for tracking state transitions and system liveness over time

Saturating math

Perform saturating math operations on integers

TimescaleDB Toolkit extends TimescaleDB with additional hyperfunctions for advanced time-series analysis. For hyperfunctions included by default in TimescaleDB, see the TimescaleDB hyperfunctions documentation.