Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures

Abstract

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations.