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

Michael D. Hunter, Haya Fatimah, Marina A. Bornovalova

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)477-505
Number of pages29
Issue number2
StatePublished - Jun 2022

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics


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