Temperature Forecasting: A Comparison between Parametric and Non-Parametric Models
The development of accurate temperature prediction models is essential for not only human life but also for agricultural, animal life, tourism, and many others. Power consumption and achieving energy efficiency in buildings also depends on temperature. Although modeling-based regression is one of the most popular approaches, it still suffers from many difficulties related to the number of available measurements, the order of the model and the non-linearity of the data. In this paper, we provide a comparison between parametric and non-parametric models for temperature forecasting. We propose three-model structures to estimate the temperature in Mumbai, the business capital of India. They are parametric (i.e. Linear Regression (LR), Multi-gene Genetic Programming (MG-GP)) and non-parametric (i.e. Artificial Neural Networks (ANN)) models. These models are tested on data collected in Mumbai for the year of 2009. The results show that multi-gene GP model performs relatively well in predicting the temperature with a high degree of accuracy compared to the LR and ANN techniques.
Artificial Neural Networks, Multi-gene Genetic Programming, Regression, Temperature forecasting
Sheta, Alaa, Ajay Katangur, Abdelkarim Baareh. "Temperature Forecasting: A Comparison between Parametric and Non-Parametric Models." Applied Mathematics & Information Sciences 12 no. 6 (2018) 1099-1108.
Applied Mathematics and Information Sciences