Signal Decomposition
3 min
the signal decomposition processor separates a time series into trend, seasonality, and residual components why use signal decomposition use this processor when you need to isolate a long term trend or a repeating seasonal pattern from a signal's noise, for example, separating a machine's underlying wear trend from its normal cyclical variation how it works the processor calculates trend using linear regression and seasonality using naive differencing, then combines them with the residual (whatever's left over) according to the model you choose in model type additive model signal = trend + seasonality + residual multiplicative model signal = trend × seasonality × residual use periodicity to set the frequency at which the seasonal component is expected to repeat, and window size to set how many values the processor considers for each calculation parameters parameter details window size how many values are included in each periodic interval for the decomposition calculation model type the decomposition model to use additive or multiplicative periodicity the frequency at which the seasonal component is expected to repeat in the data related topics use the signal decomposition function docid\ civp10gpz5ruglrelvgba