Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends
Oisín Ryan; Jonas M B Haslbeck; Lourens Waldorp · 2025 · Multivariate Behavioral Research
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.e., by differencing or detrending). However, many empirical researchers have a limited understanding of stationarity, which can lead to the use of in
Abstract by Oisín Ryan; Jonas M B Haslbeck; Lourens Waldorp, Multivariate Behavioral Research (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1080/00273171.2024.2436413