Analysis of the stationarity and correlation of the global temperature and carbon dioxide time series

Abstract

Background: The rapid and ongoing phenomenon of global warming has negatively impacted both the Earth’s environment and its inhabitants. Time series and regression analysis techniques play a significant role in weather forecasting and the interpretation of climate data. One of the key characteristics of time series analysis is stationarity. Methods: In this article, we explore how detrending and differencing techniques can be used to transform the time series of global temperature and carbon dioxide into stationary series. Regression models and goodness of fit tests were used to examine the relationship between carbon dioxide and data on global temperature. A cross-correlation time series model is also used to assess those time series’ lagging and leading characteristics. Results: The study of data on global temperature anomalies indicates that detrending and differencing are helpful in transforming temperature time series into stationary time series. However, the first differencing and detrending methods do not make the carbon dioxide time series stationary; instead, an alternate transformation is needed. Neither the carbon dioxide time series nor the global temperature time series lag or lead with regard to the cross-correlation function. Conclusions: In this article, we looked into stationarity and some other topics associated with correlation in terms of data on CO2 and global temperature. Stationarity is one of the important properties to check before conducting a more thorough investigation of the time series. To transform a non-stationary time series into a stationary one, there are numerous techniques available. However, in this article, we just pay attention to detrending and differencing and how those methods perform with respect to time series data for global temperature and carbon dioxide.