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 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 m

SUBMITTER: Loossens T 

PROVIDER: S-EPMC7973711 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature