Memory-Dependent Forecasting of COVID-19: The Flexibility of
Extrapolated Kernel Least Mean Square Algorithm
Abstract
The extrapolated kernel least mean square algorithm (extrap-KLMS) with
memory is proposed for the forecasting of future trends of COVID-19. The
extrap-KLMS is derived in the framework of data-driven modelling that
attempts to describe the dynamics of infectious disease by
reconstructing the phase-space of the state variables in a reproducing
kernel Hilbert space (RKHS). Short-time forecasting is enabled via an
extrapolation of the KLMS trained model using a forward euler step,
along the direction of a memory-dependent gradient estimate. A
user-defined memory averaging window allows users to incorporate prior
knowledge of the history of the pandemic into the gradient estimate thus
providing a spectrum of scenario-based estimates of futures trends. The
performance of the extrap-KLMS method is validated using data set for
Malaysia, Saudi Arabia and Italy in which we highlight the flexibility
of the method in capturing persistent trends of the pandemic. A
situational analysis of the Malaysian third wave further demonstrate the
capabilities of our method