timeSeriesPredictLinearToGrid

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## timeSeriesPredictLinearToGrid[​](#timeSeriesPredictLinearToGrid "Direct link to 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.

Note

This function is experimental, enable it by setting `allow_experimental_ts_to_grid_aggregate_function=true`.

**Syntax**

```
timeSeriesPredictLinearToGrid(start_timestamp, end_timestamp, grid_step, staleness, predict_offset)(timestamp, value)
```

**Parameters**

- `start_timestamp` — Specifies start of the grid. - `end_timestamp` — Specifies end of the grid. - `grid_step` — Specifies step of the grid in seconds. - `staleness` — Specifies the maximum "staleness" in seconds of the considered samples. The staleness window is a left-open and right-closed interval. - `predict_offset` — Specifies number of seconds of offset to add to prediction time.

**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**

```
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
);
```

```
┌─timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamp, value)─┐
│ [NULL,NULL,1,9.166667,11.6,16.916666,NULL,NULL,16.5]                                                            │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```

**Same query with array arguments**

```
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);
```

```
┌─timeSeriesPredictLinearToGrid(start_ts, end_ts, step_seconds, window_seconds, predict_offset)(timestamp, value)─┐
│ [NULL,NULL,1,9.166667,11.6,16.916666,NULL,NULL,16.5]                                                            │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```

- [timeSeriesPredictLinearToGrid](#timeSeriesPredictLinearToGrid)

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