A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy

Abstract Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the une...

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Autores principales:	Tim Loossens, Francis Tuerlinckx, Stijn Verdonck
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/18c52030855f4ce0a6c94c8bbd103da3
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spelling	oai:doaj.org-article:18c52030855f4ce0a6c94c8bbd103da32021-12-02T11:39:39ZA comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy10.1038/s41598-021-85320-42045-2322https://doaj.org/article/18c52030855f4ce0a6c94c8bbd103da32021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85320-4https://doaj.org/toc/2045-2322Abstract Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between observations will lead to an advantage for the OU in terms of predictive accuracy. In this paper, this is claim is being investigated by comparing the predictive accuracy of the OU model to that of the VAR(1) model on typical ESM data obtained in the context of affect research. It is shown that the VAR(1) model outperforms the OU model for the majority of the time series, even though time intervals in the data are unequally spaced. Accounting for measurement error does not change the result. Deleting large abrupt changes on short time intervals (that may be caused by externally driven events) does however lead to a significant improvement for the OU model. This suggests that processes in psychology may be continuously evolving, but that there are factors, like external events, which can disrupt the continuous flow.Tim LoossensFrancis TuerlinckxStijn VerdonckNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tim Loossens Francis Tuerlinckx Stijn Verdonck A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
description	Abstract Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between observations will lead to an advantage for the OU in terms of predictive accuracy. In this paper, this is claim is being investigated by comparing the predictive accuracy of the OU model to that of the VAR(1) model on typical ESM data obtained in the context of affect research. It is shown that the VAR(1) model outperforms the OU model for the majority of the time series, even though time intervals in the data are unequally spaced. Accounting for measurement error does not change the result. Deleting large abrupt changes on short time intervals (that may be caused by externally driven events) does however lead to a significant improvement for the OU model. This suggests that processes in psychology may be continuously evolving, but that there are factors, like external events, which can disrupt the continuous flow.
format	article
author	Tim Loossens Francis Tuerlinckx Stijn Verdonck
author_facet	Tim Loossens Francis Tuerlinckx Stijn Verdonck
author_sort	Tim Loossens
title	A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
title_short	A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
title_full	A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
title_fullStr	A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
title_full_unstemmed	A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
title_sort	comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/18c52030855f4ce0a6c94c8bbd103da3
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A comparison of continuous and discrete time modeling of affective processes in terms of predictive accuracy

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