Hyperfunctions

Timescale hyperfunctions are a specialized set of functions that allow you to analyze time-series data. You can use hyperfunctions to analyze anything you have stored as time-series data, including IoT devices, IT systems, marketing analytics, user behavior, financial metrics, and cryptocurrency.

Some hyperfunctions are included by default in Timescale. For additional hyperfunctions, you need to install the Timescale Toolkit PostgreSQL extension.

For more information, see the hyperfunctions documentation.

Approximate count distinct functions

Hyperfunction type	Hyperfunction name	Toolkit
hyperloglog() functions
Aggregate	`hyperloglog()`	✅
Alternate aggregate	`approx_count_distinct()`	✅
Accessor	`distinct_count()`	✅
Accessor	`stderror()`	✅
Rollup	`rollup()`	✅

Counters and gauges functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
counter_agg() functions
Aggregate	`counter_agg()`	✅
Accessor	`corr()`	✅
	`counter_zero_time()`	✅
	`delta()`	✅
	`extrapolated_delta()`	✅
	`extrapolated_rate()`	✅
	`first_time()`	✅
	`first_val()`	✅
	`idelta_left()`	✅
	`idelta_right()`	✅
	`intercept()`	✅
	`interpolated_delta()`	✅
	`interpolated_rate()`	✅
	`irate_left()`	✅
	`irate_right()`	✅
	`last_time()`	✅
	`last_val()`	✅
	`num_changes()`	✅
	`num_elements()`	✅
	`num_resets()`	✅
	`rate()`	✅
	`slope()`	✅
	`time_delta()`	✅
Rollup	`rollup()`	✅
Mutator	`with_bounds()`	✅
gauge_agg() functions
Aggregate	`gauge_agg()`	✅	✅
Accessor	`corr()`	✅	✅
	`delta()`	✅	✅
	`extrapolated_delta()`	✅	✅
	`extrapolated_rate()`	✅	✅
	`gauge_zero_time()`	✅	✅
	`idelta_left()`	✅	✅
	`idelta_right()`	✅	✅
	`intercept()`	✅	✅
	`interpolated_delta()`	✅	✅
	`interpolated_rate()`	✅	✅
	`irate_left()`	✅	✅
	`irate_right()`	✅	✅
	`num_changes()`	✅	✅
	`num_elements()`	✅	✅
	`rate()`	✅	✅
	`slope()`	✅	✅
	`time_delta()`	✅	✅
Rollup	`rollup()`	✅	✅
Mutator	`with_bounds()`	✅	✅

Downsampling functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
Function	`asap_smooth()`	✅
	`gp_lttb()`	✅	✅
	`lttb()`	✅

Financial analysis functions

Hyperfunction type	Hyperfunction name	Toolkit
candlestick_agg() functions
Aggregate	`candlestick_agg()`	✅
Pseudo aggregate	`candlestick()`	✅
Accessor	`close()`	✅
	`close_time()`	✅
	`high()`	✅
	`high_time()`	✅
	`low()`	✅
	`low_time()`	✅
	`open()`	✅
	`open_time()`	✅
	`volume()`	✅
	`vwap()`	✅
Rollup	`rollup()`	✅

Frequency analysis functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
count_min_sketch() functions
Aggregate	`count_min_sketch()`	✅	✅
Accessor	`approx_count()`	✅	✅
freq_agg() functions
Aggregate	`freq_agg()`	✅	✅
Accessor	`into_values()`	✅
	`max_frequency()`	✅
	`min_frequency()`	✅
	`topn()`	✅
Alternate aggregate	`mcv_agg()`	✅
Rollup	`rollup()`	✅

Gapfilling functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
Bucket	`time_bucket_gapfill()`
Interpolator	`interpolate()`
Interpolator	`locf()`

Minimum and maximum functions

Hyperfunction type	Hyperfunction name	Toolkit
max_n() functions
Aggregate	`max_n()`	✅
Accessor	`into_array()`	✅
Accessor	`into_values()`	✅
Rollup	`rollup()`	✅
max_n_by() functions
Aggregate	`max_n_by()`	✅
Accessor	`into_values()`	✅
Rollup	`rollup()`	✅
min_n() functions
Aggregate	`min_n()`	✅
Accessor	`into_array()`	✅
Accessor	`into_values()`	✅
Rollup	`rollup()`	✅
min_n_by() functions
Aggregate	`min_n_by()`	✅
Accessor	`into_values()`	✅
Rollup	`rollup()`	✅

Percentile approximation functions

Hyperfunction type	Hyperfunction name	Toolkit
tdigest() functions
Aggregate	`tdigest()`	✅
Accessor	`approx_percentile()`	✅
	`approx_percentile_rank()`	✅
	`mean()`	✅
	`num_vals()`	✅
Rollup	`rollup()`	✅
uddsketch() functions
Aggregate	`uddsketch()`	✅
Accessor	`approx_percentile()`	✅
	`approx_percentile_array()`	✅
	`approx_percentile_rank()`	✅
	`error()`	✅
	`mean()`	✅
	`num_vals()`	✅
Alternate aggregate	`percentile_agg()`	✅
Rollup	`rollup()`	✅

Saturating math functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
Function	`saturating_add()`	✅	✅
	`saturating_add_pos()`	✅	✅
	`saturating_mul()`	✅	✅
	`saturating_sub()`	✅	✅
	`saturating_sub_pos()`	✅	✅

State tracking functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
compact_state_agg() functions
Aggregate	`compact_state_agg()`	✅	✅
Accessor	`duration_in()`	✅	✅
	`interpolated_duration_in()`	✅	✅
	`into_values()`	✅	✅
Rollup	`rollup()`	✅	✅
heartbeat_agg() functions
Aggregate	`heartbeat_agg()`	✅
Accessor	`dead_ranges()`	✅
	`downtime()`	✅
	`interpolate()`	✅
	`interpolated_downtime()`	✅
	`interpolated_uptime()`	✅
	`live_at()`	✅
	`live_ranges()`	✅
	`num_gaps()`	✅
	`num_live_ranges()`	✅
	`trim_to()`	✅
	`uptime()`	✅
Rollup	`rollup()`	✅
state_agg() functions
Aggregate	`state_agg()`	✅
Accessor	`duration_in()`	✅
	`interpolated_duration_in()`	✅
	`interpolated_state_periods()`	✅
	`interpolated_state_timeline()`	✅
	`into_values()`	✅
	`state_at()`	✅
	`state_periods()`	✅
	`state_timeline()`	✅
Rollup	`rollup()`	✅

Statistical and regression analysis functions

Hyperfunction type	Hyperfunction name	Toolkit
stats_agg() (one variable) functions
Aggregate	`stats_agg() (one variable)`	✅
Accessor	`average()`	✅
	`kurtosis()`	✅
	`num_vals()`	✅
	`skewness()`	✅
	`stddev()`	✅
	`sum()`	✅
	`variance()`	✅
Rollup	`rolling()`	✅
Rollup	`rollup()`	✅
stats_agg() (two variables) functions
Aggregate	`stats_agg() (two variables)`	✅
Accessor	`average_y()`, `average_x()`	✅
	`corr()`	✅
	`covariance()`	✅
	`determination_coeff()`	✅
	`intercept()`	✅
	`kurtosis_y()`, `kurtosis_x()`	✅
	`num_vals()`	✅
	`skewness_y()`, `skewness_x()`	✅
	`slope()`	✅
	`stddev_y()`, `stddev_x()`	✅
	`sum_y()`, `sum_x()`	✅
	`variance_y()`, `variance_x()`	✅
	`x_intercept()`	✅
Rollup	`rolling()`	✅
Rollup	`rollup()`	✅

Time weighted calculations functions

Hyperfunction type	Hyperfunction name	Toolkit
time_weight() functions
Aggregate	`time_weight()`	✅
Accessor	`first_time()`	✅
	`first_val()`	✅
	`integral()`	✅
	`interpolated_average()`	✅
	`interpolated_integral()`	✅
	`last_time()`	✅
	`average()`	✅
	`last_val()`	✅
Rollup	`rollup()`	✅

General functions

Hyperfunction type	Hyperfunction name	Toolkit	Experimental
Bucket	`time_bucket()`
Bucket	`timescaledb_experimental.time_bucket_ng()`		✅
One step aggregate	`approximate_row_count()`
	`first()`
	`histogram()`
	`last()`
One step operation	`days_in_month()`	✅
One step operation	`month_normalize()`	✅