Cook Books# Data Alignment modify data, so that its index exhibits a unary sampling rate by: shifting, interpolating or aggregating it but: conserve data gap structure minimize and control value distortion from alignment back-project calculated flags from the aligned data onto the original Outlier Detection quickly set up a simple yet powerful outlier detection algorithm learn to interprete and tune the parameters Multivariate Outlier Detection apply k-nearest neighbor scoring to obtain outlier evaluation in multivariate contexts use STRAY Algorithm to find a suitable cut-off point for obtained scores Generic Expressions and Custom Functionality obtain results from arbitrary arithmetic operations on your data freely formulate logical quality control conditions Drift Detection define metrics to measure distance between data series automatically determine majority and anomalous data groups Modelling, Residuals and Arithmetics obtain data derivates through different modelling approaches like rolling statistics or curve fits obtain model errors and apply standard anomaly tests on those project the result onto the original data
