Generalized Additive Model for Count Time Series: An Application to Quantify the Impact of Air Pollutants on Human Health

Camara, Ana Julia A.; Franco, G. A. P.; Reisen, Valdério Anselmo; Bondon, Pascal

doi:10.1590/0101-7438.2021.041.00241120

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Larger penalties shrink the coefficient covariances, effectively forcing the function towards a straight line when the data do not justify a nonlinear relationship (Marra & Wood, 2011; Wood, 2016). GAMs are particularly sought after for modelling time series to identify nonlinear or time‐varying covariate effects, perform smoothing of historical time series and uncover periods of rapid change, though strong temporal autocorrelation can make it challenging to estimate key parameters (Camara et al, 2021; Knape, 2016; Simpson, 2018; Spooner et al, 2018; Yang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series

Clark

Wells

2022

Methods Ecol Evol

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1. Generalised Additive Models (GAMs) are increasingly popular for describing smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero-inflation, bounding and uneven sampling.However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data.2. We introduce Dynamic Generalised Additive Models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes.These models are particularly useful for multivariate series, as they can estimate hierarchical smooths while learning complex temporal associations via dimension-reduced latent factor processes. We implement our models in the mvgam R package, which uses the mgcv and rjags packages to construct smoothing splines and estimate unobserved parameters in a probabilistic framework.3. Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions.We use a real-world case study to highlight some of mvgam's key features, which include functions for: calculating correlations among series' latent trends, performing model selection using rolling window forecasts, online data augmentation via a recursive particle filter, and visualising probabilistic uncertainties for smooths and predictions. 4. Dynamic GAMs (DGAM) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology..

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Section: Introductionmentioning

confidence: 99%

Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series

Clark

Wells

2022

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…Larger penalties shrink the coefficient covariances, effectively forcing the smooth toward a straight line when the data do not justify a nonlinear function (Marra and Wood 2011, Wood 2016). GAMs are particularly sought after for modelling time series to both identify nonlinear or time-varying covariate effects and to uncover periods of rapid change, though strong temporal autocorrelation can make it challenging to estimate key parameters (Yang et al 2012, Knape 2016, Simpson 2018, Spooner et al 2018, Camara et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Generalised Additive Models (DGAM) for forecasting discrete ecological time series

Clark

Wells

2022

Preprint

View full text Add to dashboard Cite

Generalised Additive Models (GAMs) are increasingly popular for describing smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero-inflation, bounding and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data. We introduce Dynamic Generalised Additive Models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are particularly useful for multivariate series, as they can estimate hierarchical smooths while learning complex temporal associations via dimension-reduced latent factor processes. We implement our models in the mvgam R package, which uses the mgcv and rjags packages to construct smoothing splines and estimate unobserved parameters in a probabilistic framework. Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real-world case study to highlight some of mvgam's key features, which include functions for: calculating correlations among series' latent trends, performing model selection using rolling window forecasts, online data augmentation via a recursive particle filter, and visualising probabilistic uncertainties for smooths and predictions. Dynamic GAMs (DGAM) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology.

show abstract

Generalized Additive Model for Count Time Series: An Application to Quantify the Impact of Air Pollutants on Human Health

Cited by 2 publications

References 26 publications

Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series

Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series

Dynamic Generalised Additive Models (DGAM) for forecasting discrete ecological time series

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