An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Table 2

The name, design principle, and computing method of some features in Tsfresh.

Name	Design principle	Computing method [16, 30]
Mean	The baseline of time series
Standard deviation	The standard deviation of time series
Coefficient of variation	The reflection of the degree of data dispersion
Local fluctuation 1	The difference of the smooth curve and original curve
Local fluctuation 2	Local fluctuation with a dynamic step
Smooth factor	The ratio of the whole number to the number of turning points
Symmetrical value	The symmetry of the curve
Fluctuation ratio	Whole fluctuation power
Skewness	The estimation of the degree of statistical data distribution and the direction of skew is the digital characteristics of the asymmetric degree of statistical data distribution
approximate_entropy	Approximate entropy is used to measure the periodicity, unpredictability, and volatility of a time series	Refer to [16]
Autoregressive coefficient	Measure the cyclical nature of data
Kurtosis	The feature number indicating the peak value of the probability density distribution curve at the average value
absolute_sum_of_changes	Absolute sum of first-order difference
Linear_trend	Calculation of a linear least squares regression for the values of the time series to the sequence from 0 to the length of the time series −1	Refer to [16]
fft_aggregated	Returns the variance, mean, kurtosis, skewness, and absolute Fourier transform spectrum	Refer to [16]