  Generally, anomaly detection has a great importance particularly in applied
statistical signal processing. Here we provide a general framework in order to
detect anomaly through the statistical modeling. In this paper, it is assumed
that a signal is corrupted by noise whose variance follows an ARMA model. The
assumption on the signal is further compromised to encompass the inherent
nonstationarity associated with natural phenomenon, hence, the signal of
interest is assumed to follow an ARIMA model and the noise to denote an
anomaly, however, unknown. Anomaly is assumed to possess heteroskedastic
properties, therefore, ARCH/GARCH modeling could extract the anomaly pattern
given an additive model for signal of interest and anomaly.
