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

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,

SUBMITTER: Hunter MD 

PROVIDER: S-EPMC9177592 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature