Bayesian semi-parametric forecasting of ultrafine particle number   concentration with penalised splines and autoregressive errors
  Observational time series data often exhibit both cyclic temporal trends and autocorrelation and may also depend on covariates. As such, there is a need for flexible regression models that are able to capture these trends and model any residual autocorrelation simultaneously. Modelling the autocorrelation in the residuals leads to more realistic forecasts than an assumption of independence. In this paper we propose a method which combines spline-based semi-parametric regression modelling with the modelling of auto-regressive errors.   The method is applied to a simulated data set in order to show its efficacy and to ultrafine particle number concentration in Helsinki, Finland, to show its use in real world problems. 
Sam Clifford, Bjarke M{\o}lgaard, Sama Low Choy, Jukka Corander,   Kaarle H\"ameri, Kerrie Mengersen and Tareq Hussein
stat.AP physics.ao-ph physics.data-an stat.ME