Using ARIMA and Random Forest Models for Climatic Datasets Forecasting

Abstract

The climatic changes have important role which may lead to huge problems for the health of human and other organisms, therefore it is necessary to study and forecast this type of datasets to reduce . the damages through planning and controlling for these changes in the future. The main problem can be summarized in the nonlinearity of climatic dataset and its chaotic changes. The common approach is the integrated autoregressive and moving average model (ARIMA) as traditional univariate time series approach. Therefore, more appropriate model for studying the climatic data has been proposed for obtaining more accurate forecasting, it can be called random forest (RF) model.This model cannot deal with nonlinear data correctly and that may lead to inaccurate forecasting results. In this thesis, climatic datasets are studied represented by minimum air temperature and rational humidity for agricultural meteorological station in Nineveh. This thesis aims to satisfy data homogeneity through different seasons and find suitable model deal with nonlinear data correctly with minimal forecasting error comparing to ARIMA as traditional model. The research found the adequate of the model for this type of data, as it was found that there are some factors that contribute to the increase in the number of deaths in the epidemic, such as the advanced age of the patient, the length of stay in the hospital, the percentage of oxygen in the patient's blood, in addition to the incidence of some chronic diseases such as asthma. The study recommended a more in-depth study of other types of these models, and the use of other estimation methods, in addition to paying attention to the methods of data recording by the city health department.

Keywords

• hierarchical poisson regression model with random intercept
• full maximum likelihood method
• intraclass correlation coefficient
• fixed and random effects