Time-Series-Based Air Temperature Forecasting Based on the Outlier Robust Extreme Learning Machine

Abstract

In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air temperature for one to ten hours in advance after finding its most optimum values (i.e., orthogonality effect, activation function, regularization parameter, and the number of hidden neurons). The results showed that the proposed IORELM has an acceptable degree of accuracy in predicting hourly temperatures ten hours in advance (R = 0.95; NSE = 0.89; RMSE = 3.74; MAE = 1.92).

Keywords

• extreme learning machine (ELM)
• hourly air temperature forecasting
• machine learning
• outlier robust extreme learning machine (ORELM)
• Quebec
• real-time forecasting