> ## Documentation Index > Fetch the complete documentation index at: https://private-7c7dfe99-trino-dialect.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > Aggregate function that calculates PromQL-like linear prediction over time series data on the specified grid. # timeSeriesPredictLinearToGrid

timeSeriesPredictLinearToGrid

Introduced in: v25.6.0 Aggregate function that takes time series data as pairs of timestamps and values and calculates a [PromQL-like linear prediction](https://prometheus.io/docs/prometheus/latest/querying/functions/#predict_linear) with a specified prediction timestamp offset from this data on a regular time grid described by start timestamp, end timestamp and step. For each point on the grid the samples for calculating `predict_linear` are considered within the specified time window. If several samples have the same timestamp, only one of them is used: the sample with the greatest value. A NaN value loses to any other value, so a NaN value is used only if all samples at this timestamp are NaN. This function is experimental, enable it by setting `allow_experimental_time_series_aggregate_functions=true`. **Syntax** ```sql theme={null} timeSeriesPredictLinearToGrid(start_timestamp, end_timestamp, grid_step, staleness, predict_offset)(timestamp, value) ``` **Parameters** * `start_timestamp` — Specifies start of the grid. With a `DateTime64` timestamp argument it can also be a fractional number, or a string containing a number or a date-time text. [`UInt32`](/reference/data-types/int-uint) or [`DateTime`](/reference/data-types/datetime) or [`DateTime64`](/reference/data-types/datetime64) or [`Float*`](/reference/data-types/float) or [`Decimal*`](/reference/data-types/decimal) or [`String`](/reference/data-types/string) * `end_timestamp` — Specifies end of the grid. With a `DateTime64` timestamp argument it can also be a fractional number, or a string containing a number or a date-time text. [`UInt32`](/reference/data-types/int-uint) or [`DateTime`](/reference/data-types/datetime) or [`DateTime64`](/reference/data-types/datetime64) or [`Float*`](/reference/data-types/float) or [`Decimal*`](/reference/data-types/decimal) or [`String`](/reference/data-types/string) * `grid_step` — Specifies step of the grid in seconds. With a `DateTime64` timestamp argument it can also be a fractional number, or a string containing a number or a duration like '15s' or '1m'. [`UInt32`](/reference/data-types/int-uint) or [`Float*`](/reference/data-types/float) or [`Decimal*`](/reference/data-types/decimal) or [`String`](/reference/data-types/string) * `staleness` — Specifies the maximum "staleness" in seconds of the considered samples. The staleness window is a left-open and right-closed interval. With a `DateTime64` timestamp argument it can also be a fractional number, or a string containing a number or a duration like '15s' or '1m'. [`UInt32`](/reference/data-types/int-uint) or [`Float*`](/reference/data-types/float) or [`Decimal*`](/reference/data-types/decimal) or [`String`](/reference/data-types/string) * `predict_offset` — Specifies number of seconds of offset to add to prediction time. [`UInt32`](/reference/data-types/int-uint) or [`Float*`](/reference/data-types/float) or [`Decimal*`](/reference/data-types/decimal) or [`String`](/reference/data-types/string) **Arguments** * `timestamp` — Timestamp of the sample. Can be individual values or arrays. - `value` — Value of the time series corresponding to the timestamp. Can be individual values or arrays. **Returned value** `predict_linear` values on the specified grid as an `Array(Nullable(Float64))`. The returned array contains one value for each time grid point. The value is NULL if there are not enough samples within the window to calculate the rate value for a particular grid point. **Examples** **Calculate predict\_linear values on the grid \[90, 105, 120, 135, 150, 165, 180, 195, 210] with a 60 second offset** ```sql title=Query theme={null} SET allow_experimental_time_series_aggregate_functions = 1; WITH -- NOTE: the gap between 140 and 190 is to show how values are filled for ts = 150, 165, 180 according to window parameter [110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps, [1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values, -- array of values corresponding to timestamps above 90 AS start_ts, -- start of timestamp grid 90 + 120 AS end_ts, -- end of timestamp grid 15 AS step_seconds, -- step of timestamp grid 45 AS window_seconds, -- "staleness" window 60 AS predict_offset -- prediction time offset SELECT timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamp, value) FROM ( -- This subquery converts arrays of timestamps and values into rows of `timestamp`, `value` SELECT arrayJoin(arrayZip(timestamps, values)) AS ts_and_val, ts_and_val.1 AS timestamp, ts_and_val.2 AS value ); ``` ```response title=Response theme={null} ┌─timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamp, value)─┐ │ [NULL,NULL,1,9.166667,11.6,12.5,NULL,NULL,16.5] │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ ``` **Same query with array arguments** ```sql title=Query theme={null} SET allow_experimental_time_series_aggregate_functions = 1; WITH [110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps, [1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values, 90 AS start_ts, 90 + 120 AS end_ts, 15 AS step_seconds, 45 AS window_seconds, 60 AS predict_offset SELECT timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamps, values); ``` ```response title=Response theme={null} ┌─timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamps, values)─┐ │ [NULL,NULL,1,9.166667,11.6,12.5,NULL,NULL,16.5] │ └───────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ ```