mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-29 02:52:13 +00:00
166 lines
5.8 KiB
Markdown
166 lines
5.8 KiB
Markdown
---
|
|
slug: /en/sql-reference/functions/time-series-functions
|
|
sidebar_position: 172
|
|
sidebar_label: Time Series
|
|
---
|
|
|
|
# Time Series Functions
|
|
|
|
Below functions are used for series data analysis.
|
|
|
|
## seriesOutliersDetectTukey
|
|
|
|
Detects outliers in series data using [Tukey Fences](https://en.wikipedia.org/wiki/Outlier#Tukey%27s_fences).
|
|
|
|
**Syntax**
|
|
|
|
``` sql
|
|
seriesOutliersDetectTukey(series);
|
|
seriesOutliersDetectTukey(series, min_percentile, max_percentile, K);
|
|
```
|
|
|
|
**Arguments**
|
|
|
|
- `series` - An array of numeric values.
|
|
- `min_percentile` - The minimum percentile to be used to calculate inter-quantile range [(IQR)](https://en.wikipedia.org/wiki/Interquartile_range). The value must be in range [0.02,0.98]. The default is 0.25.
|
|
- `max_percentile` - The maximum percentile to be used to calculate inter-quantile range (IQR). The value must be in range [0.02,0.98]. The default is 0.75.
|
|
- `K` - Non-negative constant value to detect mild or stronger outliers. The default value is 1.5.
|
|
|
|
At least four data points are required in `series` to detect outliers.
|
|
|
|
**Returned value**
|
|
|
|
- Returns an array of the same length as the input array where each value represents score of possible anomaly of corresponding element in the series. A non-zero score indicates a possible anomaly. [Array](../data-types/array.md).
|
|
|
|
**Examples**
|
|
|
|
Query:
|
|
|
|
``` sql
|
|
SELECT seriesOutliersDetectTukey([-3, 2, 15, 3, 5, 6, 4, 5, 12, 45, 12, 3, 3, 4, 5, 6]) AS print_0;
|
|
```
|
|
|
|
Result:
|
|
|
|
``` text
|
|
┌───────────print_0─────────────────┐
|
|
│[0,0,0,0,0,0,0,0,0,27,0,0,0,0,0,0] │
|
|
└───────────────────────────────────┘
|
|
```
|
|
|
|
Query:
|
|
|
|
``` sql
|
|
SELECT seriesOutliersDetectTukey([-3, 2, 15, 3, 5, 6, 4.50, 5, 12, 45, 12, 3.40, 3, 4, 5, 6], 0.2, 0.8, 1.5) AS print_0;
|
|
```
|
|
|
|
Result:
|
|
|
|
``` text
|
|
┌─print_0──────────────────────────────┐
|
|
│ [0,0,0,0,0,0,0,0,0,19.5,0,0,0,0,0,0] │
|
|
└──────────────────────────────────────┘
|
|
```
|
|
|
|
## seriesPeriodDetectFFT
|
|
|
|
Finds the period of the given series data data using FFT
|
|
FFT - [Fast Fourier transform](https://en.wikipedia.org/wiki/Fast_Fourier_transform)
|
|
|
|
**Syntax**
|
|
|
|
``` sql
|
|
seriesPeriodDetectFFT(series);
|
|
```
|
|
|
|
**Arguments**
|
|
|
|
- `series` - An array of numeric values
|
|
|
|
**Returned value**
|
|
|
|
- A real value equal to the period of series data. NaN when number of data points are less than four. [Float64](../data-types/float.md).
|
|
|
|
**Examples**
|
|
|
|
Query:
|
|
|
|
``` sql
|
|
SELECT seriesPeriodDetectFFT([1, 4, 6, 1, 4, 6, 1, 4, 6, 1, 4, 6, 1, 4, 6, 1, 4, 6, 1, 4, 6]) AS print_0;
|
|
```
|
|
|
|
Result:
|
|
|
|
``` text
|
|
┌───────────print_0──────┐
|
|
│ 3 │
|
|
└────────────────────────┘
|
|
```
|
|
|
|
``` sql
|
|
SELECT seriesPeriodDetectFFT(arrayMap(x -> abs((x % 6) - 3), range(1000))) AS print_0;
|
|
```
|
|
|
|
Result:
|
|
|
|
``` text
|
|
┌─print_0─┐
|
|
│ 6 │
|
|
└─────────┘
|
|
```
|
|
|
|
## seriesDecomposeSTL
|
|
|
|
Decomposes a series data using STL [(Seasonal-Trend Decomposition Procedure Based on Loess)](https://www.wessa.net/download/stl.pdf) into a season, a trend and a residual component.
|
|
|
|
**Syntax**
|
|
|
|
``` sql
|
|
seriesDecomposeSTL(series, period);
|
|
```
|
|
|
|
**Arguments**
|
|
|
|
- `series` - An array of numeric values
|
|
- `period` - A positive integer
|
|
|
|
The number of data points in `series` should be at least twice the value of `period`.
|
|
|
|
**Returned value**
|
|
|
|
- An array of four arrays where the first array include seasonal components, the second array - trend,
|
|
the third array - residue component, and the fourth array - baseline(seasonal + trend) component. [Array](../data-types/array.md).
|
|
|
|
**Examples**
|
|
|
|
Query:
|
|
|
|
``` sql
|
|
SELECT seriesDecomposeSTL([10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34], 3) AS print_0;
|
|
```
|
|
|
|
Result:
|
|
|
|
``` text
|
|
┌───────────print_0──────────────────────────────────────────────────────────────────────────────────────────────────────┐
|
|
│ [[
|
|
-13.529999, -3.1799996, 16.71, -13.53, -3.1799996, 16.71, -13.53, -3.1799996,
|
|
16.71, -13.530001, -3.18, 16.710001, -13.530001, -3.1800003, 16.710001, -13.530001,
|
|
-3.1800003, 16.710001, -13.530001, -3.1799994, 16.71, -13.529999, -3.1799994, 16.709997
|
|
],
|
|
[
|
|
23.63, 23.63, 23.630003, 23.630001, 23.630001, 23.630001, 23.630001, 23.630001,
|
|
23.630001, 23.630001, 23.630001, 23.63, 23.630001, 23.630001, 23.63, 23.630001,
|
|
23.630001, 23.63, 23.630001, 23.630001, 23.630001, 23.630001, 23.630001, 23.630003
|
|
],
|
|
[
|
|
0, 0.0000019073486, -0.0000019073486, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0000019073486, 0,
|
|
0
|
|
],
|
|
[
|
|
10.1, 20.449999, 40.340004, 10.100001, 20.45, 40.34, 10.100001, 20.45, 40.34, 10.1, 20.45, 40.34,
|
|
10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.100002, 20.45, 40.34
|
|
]] │
|
|
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
|
|
```
|