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166 lines
5.8 KiB
Markdown
166 lines
5.8 KiB
Markdown
---
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slug: /en/sql-reference/functions/time-series-functions
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sidebar_position: 172
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sidebar_label: Time Series
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---
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# Time Series Functions
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Below functions are used for series data analysis.
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## seriesOutliersDetectTukey
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Detects outliers in series data using [Tukey Fences](https://en.wikipedia.org/wiki/Outlier#Tukey%27s_fences).
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**Syntax**
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``` sql
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seriesOutliersDetectTukey(series);
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seriesOutliersDetectTukey(series, min_percentile, max_percentile, K);
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```
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**Arguments**
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- `series` - An array of numeric values.
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- `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.
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- `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.
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- `K` - Non-negative constant value to detect mild or stronger outliers. The default value is 1.5.
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At least four data points are required in `series` to detect outliers.
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**Returned value**
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- 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).
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**Examples**
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Query:
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``` sql
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SELECT seriesOutliersDetectTukey([-3, 2, 15, 3, 5, 6, 4, 5, 12, 45, 12, 3, 3, 4, 5, 6]) AS print_0;
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```
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Result:
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``` text
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┌───────────print_0─────────────────┐
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│[0,0,0,0,0,0,0,0,0,27,0,0,0,0,0,0] │
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└───────────────────────────────────┘
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```
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Query:
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``` sql
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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;
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```
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Result:
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``` text
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┌─print_0──────────────────────────────┐
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│ [0,0,0,0,0,0,0,0,0,19.5,0,0,0,0,0,0] │
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└──────────────────────────────────────┘
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```
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## seriesPeriodDetectFFT
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Finds the period of the given series data data using FFT
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FFT - [Fast Fourier transform](https://en.wikipedia.org/wiki/Fast_Fourier_transform)
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**Syntax**
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``` sql
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seriesPeriodDetectFFT(series);
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```
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**Arguments**
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- `series` - An array of numeric values
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**Returned value**
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- 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).
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**Examples**
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Query:
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``` sql
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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;
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```
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Result:
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``` text
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┌───────────print_0──────┐
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│ 3 │
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└────────────────────────┘
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```
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``` sql
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SELECT seriesPeriodDetectFFT(arrayMap(x -> abs((x % 6) - 3), range(1000))) AS print_0;
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```
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Result:
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``` text
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┌─print_0─┐
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│ 6 │
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└─────────┘
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```
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## seriesDecomposeSTL
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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.
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**Syntax**
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``` sql
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seriesDecomposeSTL(series, period);
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```
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**Arguments**
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- `series` - An array of numeric values
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- `period` - A positive integer
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The number of data points in `series` should be at least twice the value of `period`.
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**Returned value**
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- An array of four arrays where the first array include seasonal components, the second array - trend,
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the third array - residue component, and the fourth array - baseline(seasonal + trend) component. [Array](../data-types/array.md).
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**Examples**
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Query:
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``` sql
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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;
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```
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Result:
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``` text
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┌───────────print_0──────────────────────────────────────────────────────────────────────────────────────────────────────┐
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│ [[
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-13.529999, -3.1799996, 16.71, -13.53, -3.1799996, 16.71, -13.53, -3.1799996,
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16.71, -13.530001, -3.18, 16.710001, -13.530001, -3.1800003, 16.710001, -13.530001,
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-3.1800003, 16.710001, -13.530001, -3.1799994, 16.71, -13.529999, -3.1799994, 16.709997
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],
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[
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23.63, 23.63, 23.630003, 23.630001, 23.630001, 23.630001, 23.630001, 23.630001,
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23.630001, 23.630001, 23.630001, 23.63, 23.630001, 23.630001, 23.63, 23.630001,
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23.630001, 23.63, 23.630001, 23.630001, 23.630001, 23.630001, 23.630001, 23.630003
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],
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[
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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,
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0
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],
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[
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10.1, 20.449999, 40.340004, 10.100001, 20.45, 40.34, 10.100001, 20.45, 40.34, 10.1, 20.45, 40.34,
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10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.1, 20.45, 40.34, 10.100002, 20.45, 40.34
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]] │
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└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
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```
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