Feature Extractor

the feature extractor processor calculates statistical features, such as the average, standard deviation, and skewness, from a window of raw values, turning raw signal data into inputs that analytics and machine learning models can use directly why use feature extractor use this processor to derive statistical features from a signal instead of calculating windowed statistics like variance or kurtosis yourself, for example, to feed a machine learning model with pre computed features rather than raw values how it works set a window size and the processor continuously recalculates each enabled statistic over that many seconds of incoming values enable only the statistics your downstream model or flow needs each one adds a field to the output payload if your input tag doesn't publish at a steady rate, use timer interval to publish a value on a fixed schedule instead of waiting for the next incoming event if your input already publishes at the interval you expect, enter 0 to disable the timer parameters parameter details window size the window, in seconds, over which the processor calculates each enabled statistic minimum the minimum value in the window maximum the maximum value in the window average the mean of the values in the window standard deviation a measure of how spread out the values in the window are, calculated as sqrt(σ(x average)² / (n 1)) variance the squared measure of spread within the window, calculated as σ(x average)² / (n 1) median the middle value in the window for an odd number of values, this is the center value for an even number, it's the average of the two center values kurtosis measures the "heaviness" of the tails of the value distribution, calculated as \[n(n+1) / (n 1)(n 2)(n 3)] × σ\[(xᵢ average) / stddeviation]⁴ skewness measures the asymmetry of the value distribution, calculated as \[n / (n 1)(n 2)] × σ\[(xᵢ average) / stddeviation]³ zero crossing rate the number of times the signal crosses zero, that is, changes from a positive to a negative value or a negative to a positive value, within the window root mean square calculated as sqrt(σx² / n) quartiles divides the window into four equal parts based on the median inter quartile range the difference between the third and first quartiles mean absolute deviation the mean of the absolute deviations from the average, calculated as σ|x average| / n average absolute variation the average absolute variation of the values in the window from the average timer interval sets a timer, in milliseconds, that continuously publishes the output when no event triggers a calculation enter 0 to disable the timer pass through value determines whether the processor's output is the calculated features or the original unchanged input value related topics use the feature extractor function docid\ hy2el1ijhticbiaji2a7x