Negligent killing of scientific concepts: the stationarity case

By Demetris Koutsoyiannis, Alberto Montanari. 2015

226 citations recorded by the source corpus.

Abstract In scientific vocabulary, the term “process” is used to denote change in time. Even a stationary process describes a system changing in time, rather than a static one that keeps a constant state all the time. However, this is often missed, which has led to misuse of the term “nonstationarity” as a synonym of “change”. A simple rule to avoid such misuse is to answer the question: can the change be predicted in deterministic terms? Only if the answer is positive is it legitimate to invoke nonstationarity. In addition, we should have in mind that models are made to simulate the future rather than to describe the past; the past is characterized by observations (data). Usually future changes are not deterministically predictable and thus the models should, on the one hand, be stationary and, on the other hand, describe in stochastic terms the full variability, originating from all agents of change. Even if the past evolution of the process of interest contains changes explainable in deterministic terms (e.g. urbanization), it is better to describe the future conditions in stationary terms, after “stationarizing” the past observations, i.e. adapting them to represent the future conditions.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/d6cef930819bb2427e102f34e9adb7cb17a10b1b

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, methodology): “If non-stationarity is assumed, then the system will not have ergodic properties and, Koutsoyiannis and Montanari (2015) suggest, inference will be impossible.”
Hydrological uncertainty processor based on a copula function (2018, background): “In other words, there is no a statistical difference between these 1 + N flow series (Koutsoyiannis and Montanari 2015).”