1. A method for compressing heteroscedastic data, said method comprising: obtaining said heteroscedastic data; applying said heteroscedastic data to a combined Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, wherein said ARIMA is modified by GARCH predictions to generate heteroscedastic predictions; transforming at least a portion of said heteroscedastic data and said heteroscedastic predictions to a reversible format, independently of prior knowledge about possible values of said heteroscedastic data and said heteroscedastic predictions, wherein residuals between said heteroscedastic data and said heteroscedastic predictions have a lower entropy than said heteroscedastic data and wherein said heteroscedastic data is rebuilt substantially without a loss of precision, based on said heteroscedastic predictions and said residuals; and compressing parameters of said ARIMA-GARCH model and said residuals using entropy encoding to generate compressed residual data, wherein said compressed residual data is one or more of stored, transmitted and processed.