A Deep Learning Framework for Temperature Forecasting

Abstract

Among many global warming issues, the increase in global temperatures causing summer heatwaves have triggered heat-strokes leading to untimely deaths of thousands of people. Heatwaves are meteorological events with prolonged periods of excessive heat. Machine learning algorithms such as Auto-Regressive Integrated Moving Average (ARIMA) and Ensemble-learning and Long Short-term Memory Network (LSTM) have recently been used to forecast weather conditions. Optimizing the hyperparameters for accurate temperature forecasting is challenging. This paper presents Cauchy Particle-swarm optimization (CPSO) technique for finding the hyperparameters of the LSTM. The proposed technique minimizes the validation mean square error rate (MSER) to improve accuracy. We test the proposed technique on 30-year Riyadh city temperature datasets. In our experimental evaluation, the proposed CPSO-LSTM outperforms LSTM and Grid-search LSTM by 50% and 55% respectively.