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slug | sidebar_position | sidebar_label |
---|---|---|
/en/sql-reference/functions/time-series-functions | 172 | Time Series |
Time Series Functions
Below functions are used for series data analysis.
seriesOutliersDetectTukey
Detects outliers in series data using Tukey Fences.
Syntax
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). 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.
Type: Array.
Examples
Query:
SELECT seriesOutliersDetectTukey([-3, 2, 15, 3, 5, 6, 4, 5, 12, 45, 12, 3, 3, 4, 5, 6]) AS print_0;
Result:
┌───────────print_0─────────────────┐
│[0,0,0,0,0,0,0,0,0,27,0,0,0,0,0,0] │
└───────────────────────────────────┘
Query:
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:
┌─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
Syntax
seriesPeriodDetectFFT(series);
Arguments
series
- An array of numeric values
Returned value
- A real value equal to the period of series data
- Returns NAN when number of data points are less than four.
Type: Float64.
Examples
Query:
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:
┌───────────print_0──────┐
│ 3 │
└────────────────────────┘
SELECT seriesPeriodDetectFFT(arrayMap(x -> abs((x % 6) - 3), range(1000))) AS print_0;
Result:
┌─print_0─┐
│ 6 │
└─────────┘
seriesDecomposeSTL
Decomposes a series data using STL (Seasonal-Trend Decomposition Procedure Based on Loess) into a season, a trend and a residual component.
Syntax
seriesDecomposeSTL(series, period);
Arguments
series
- An array of numeric valuesperiod
- 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.
Type: Array.
Examples
Query:
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:
┌───────────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
]] │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘