Model-free prognostication of non-linear time series.

ABSTRACT:

Objective

The COVID-19 pandemic has highlighted the importance of studying the course of infectious progression. Similar needs exist for time series of other origins. While models are commonly devised and fitted to the observed data, we recently demonstrated the feasibility to directly evaluate the noisy non-linear time series that characterize the occurrence. However, for practical utility, analytics alone has limited value. The requirement of forecasting - at least in the short term - needs to be met.

Methods

We initially utilized normalized new infections per day (7-day moving average for cases per million inhabitants) from Our World in Data. We then validated our method in unrelated non-linear time series of stock markets and blowfly populations. We studied a novel model

SUBMITTER: Wu X 

PROVIDER: S-EPMC12863698 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature