Chaos theory modelling for temperature time series at Malaysian high population area during dry season

Abstract

The aim of this study is to model the temperature time series at Malaysian high population area during dry season through chaos theory. The selected high population area is Shah Alam located in Selangor state of Malaysia. Chaos theory modelling is categorized into two parts namely analysis and prediction. Analysis by the phase space plot showed that the nature of the observed temperature time series is chaos. Hence, the time series is predicted via the chaotic model. Results from the chaotic model showed that the temperature time series is well predicted with Pearson correlation coefficient near to 1. The result is compared with the traditional method of autoregressive linear model. Based on the computed values of average absolute error, root mean squared error and Pearson correlation coefficient, the chaotic model is found better in predicting temperature time series at Shah Alam area during dry season. This indicates that the chaos theory is applicable for temperature time series at Malaysian high population area. This finding is expected to facilitate stakeholders such as Malaysian Meteorological Department and Department of Environment Malaysia in managing temperature and climate change problem.