Relative humidity prediction using temperature and unit autoregressive moving average models

Abstract This study investigates the forecasting of hourly relative humidity (RH) using temperature as an exogenous variable, based on high-frequency environmental data collected from an IoT-enabled weather station installed in a vineyard in Italy. For this purpose, we fit and compare the predictive performance of classical autoregressive moving average (ARMA) models tailored for random variables bounded within the standard unit interval, so-called unit ARMA models, and the classical autoregressive integrated moving average (ARIMA) models. Advances in the unit ARMA literature include the beta ARMA ( $$\beta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> ARMA), Kumaraswamy ARMA (KARMA), unit Burr XII ARMA (UBXII-ARMA), and unit Weibull ARMA (UWARMA) models. All models were fitted with temperature as a covariate, which showed a significant–predominantly negative–effect across all model classes. Among the fitted models, UWARMA with temperature emerged as the most competitive, combining strong forecasting performance with theoretical consistency for modeling bounded variables. These results highlight the suitability of unit ARMA models for accurate RH forecasting in environmental monitoring applications.