Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data
Michael D. Hunter,
Haya Fatimah and
Marina A. Bornovalova
Additional contact information
Michael D. Hunter: Pennsylvania State University
Haya Fatimah: University of South Florida
Marina A. Bornovalova: University of South Florida
Psychometrika, 2022, vol. 87, issue 2, No 5, 477-505
Abstract With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.
Keywords: dynamical systems; forecasting; time series; Kalman filtering; intensive longitudinal data; drug and alcohol use (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11336-021-09827-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09827-5
Ordering information: This journal article can be ordered from
http://www.springer. ... gy/journal/11336/PS2
Access Statistics for this article
Psychometrika is currently edited by Irini Moustaki
More articles in Psychometrika from Springer, The Psychometric Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().