Incomplete angular time series imputation with a projected normal autoregressive process and exogenous predictors
Year: 2026
Type: dataset
Abstract: Air pollution and associated meteorological conditions, including wind direction, wind speed, temperature, and pressure, are typically collected and reported at regular intervals by monitoring stations. The data produced by these monitoring stations can be incomplete due to technical/mechanical errors, systemic issues (recording only once every 3 hours rather than hourly), or other potential complications. In this paper, we develop a novel imputation method for incomplete angular time series by imposing an autoregressive structure on the projected normal distribution. The imputations can then be used in a multiple imputation scheme to create several completed data sets and several corresponding fitted models with Rubin's rules or MCMC posterior stacking to combine the estimates. The proposed method was validated using simulation studies based on autoregressive regression models for a simulated continuous response with a single angular predictor and a single continuous predictor. We used our proposed imputation methods to model daily PM2.5 concentrations with wind direction and wind speed as predictors. The data are collected from the EPA's Air Quality System.
Source: Figshare
Authors: Benjamin Stockton, Ofer Harel
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7211941459