Signal Decomposition

the signal decomposition processor separates signal components from composite signals related to semantic units examples for this are distinct objects in images or video, video shots, melody sequences in music, spoken words or sentences in speech signals signal decomposition overview native version of signal decomposition additive model signal = trend + seasonality + residue multiplicative model signal = trend seasonality residue trend is calculated by linear regression seasonality is calculated by naive differencing signal decomposition parameters parameters details window size this parameter specifies how many values are included in the periodic interval for the signal decomposition process model type this parameter defines the type of model to be used for the signal decomposition periodicity this parameter determines the periodic interval for the signal decomposition, providing the frequency at which the seasonal components are expected to repeat in the data note when creating an analytics flow with signal decomposition processor, refer the docid\ gmeoyablwo kuxtc5vjv4 guide for more details