Functionality Overview# Anomaly Detection Basic Anomaly Detection data gaps, data jumps, isolated points, constant and low variance regimes. Outlier Detection rolling Z-score cutoff modified local outlier factor (univariate-LOF) deterministic offset pattern search Multivariate Analysis k-nearest neighbor scores (kNN) local outlier factor (LOF) Distributional Analysis detect change points detect continuous noisy data sections Data and Flag Tools Data Independent Flags Manipulation copy flags transfer flags propagate flags force-set unitary or precalculated flags values Basic tools plot variables copy and delete variables Generic and Custom Functions basic logical aggregation of variables basic arithmetical aggregation of variables custom functions rolling, resampling, transformation Data Manipulation Data Products smooth with frequency filter smooth with polynomials obtain residuals from smoothing obtain kNN or LOF scores Resampling resample data using custom aggregation align data to frequency grid with minimal data distortion back project flags from aligned data onto original series Data Correction Gap filling fill gaps with interpolations fill gaps using a rolling window Drift Detection and Correction deviation predicted by a model deviation from the majority of parallel curves deviation from a defined norm curve
