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.
Negligent killing of scientific concepts: the stationarity case / Koutsoyiannis, Demetris; Montanari, Alberto. - In: HYDROLOGICAL SCIENCES JOURNAL. - ISSN 0262-6667. - ELETTRONICO. - 60:7-8(2015), pp. 1174-1183. [10.1080/02626667.2014.959959]
Negligent killing of scientific concepts: the stationarity case
MONTANARI, ALBERTO
2015
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.