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Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data

Published online by Cambridge University Press:  01 January 2025

Michael D. Hunter Open the ORCID record for Michael D. Hunter [Opens in a new window] ,
Haya Fatimah  and
Marina A. Bornovalova
Michael D. Hunter*
Affiliation:
Pennsylvania State University
Haya Fatimah
Affiliation:
University of South Florida
Marina A. Bornovalova
Affiliation:
University of South Florida
*
Correspondence should be made to Michael D. Hunter, Department of Human Development and Family Studies, Pennsylvania State University, 119 Health and Human Development Building, University Park, PA16802, USA. Email: mhunter.ou@gmail.com
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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 systemsforecastingtime seriesKalman filteringintensive longitudinal datadrug and alcohol use
Type
Theory and Methods
Information
Psychometrika , Volume 87 , Issue 2: Special Issue on Forecasting with Intensive Longitudinal Data , June 2022 , pp. 477 - 505
Copyright
copyright © 2022 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

The work was supported by DA032582 (NIDA)

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