A Data-Driven SPC Framework for Monitoring and Forecasting Global Temperature Trends

This study aims to develop an enhanced Statistical Process Control (SPC) framework for the effective monitoring and forecasting of global temperature trends, addressing the limitations of traditional control charts in handling autocorrelated environmental data. The primary objective is to construct a modified Exponentially Weighted Moving Average (EWMA) chart capable of detecting both abrupt and gradual shifts in time series exhibiting seasonal and stochastic dependencies. The proposed approach models global temperature data using a Seasonal Autoregressive Moving Average process with exponential white noise (SARMA(1,1)L) to capture temporal patterns and residual variability. An analytical formulation for evaluating the one sided Average Run Length (ARL) is derived, enabling quantitative assessment of chart performance. The simulation analysis demonstrates that, by applying an optimized smoothing parameter, the proposed EWMA control chart outperforms the traditional EWMA and CUSUM charts, particularly in detecting small shifts with significantly lower ARL1 values. The findings confirm that the proposed model effectively tracks and predicts global temperature anomalies with high sensitivity and accuracy. The novelty of this research lies in integrating SARMA-based modeling with SPC design to improve detection reliability under autocorrelated and nonstationary conditions. This data-driven framework offers a promising tool for real-time environmental monitoring and climate forecasting applications.