A Comprehensive Time Series Analysis of Seasonal and Trend Patterns In Temperature Data of Visakhapatnam

Abstract

This research presents an in-depth analysis of urban temperature data through advanced time series decomposition and statistical modelling, aiming to elucidate the complex dynamics of temperature variations within an urban context. Employing a robust methodology that combines seasonal decomposition, the Augmented Dickey-Fuller test for stationarity, and autocorrelation analyses, the study comprehensively explores both the predictable and random components of temperature fluctuations. Key findings indicate that the trend accurately captures the central tendencies and seasonal patterns of urban temperatures, with most predictions falling within an acceptable range of the actual measurements. The seasonal and trend components of the time series reveal consistent long-term patterns and clear seasonal variations, essential for understanding and forecasting weather changes. Additionally, the analysis of residuals, particularly through Kernel Density Estimation and boxplots, highlights the occurrences of extreme temperature deviations and identifies potential areas for refinement. This study contributes significantly to the fields of urban climatology and meteorological forecasting by providing detailed insights into the microclimatic conditions of urban areas. The findings underscore the importance of understanding temperature variability and extremes, especially in light of changing climate patterns and urban development. Furthermore, the research identifies pathways for future work, emphasizing integrating additional environmental factors, exploring more sophisticated modelling techniques, and enhancing predictive capabilities for extreme weather events. Overall, this research offers valuable implications for urban planners, policymakers, and climate scientists in devising strategies to mitigate the impacts of extreme temperatures and improve urban living conditions