<![CDATA[This study analyzes the daily average temperature data of Delhi city from 2013 to 2017 using the Autoregressive Integrated Moving Average (ARIMA) model to model and predict temperature trends. The temperature data processed in this study is non-stationary, so differentiation is applied to achieve stationarity. Two ARIMA models were evaluated: ARIMA (1,1,1) and ARIMA (1,1,1)(1,0,1). The ARIMA (1,1,1) model is effective in capturing short-term patterns, while the ARIMA (1,1,1)(1,0,1) model performs better in handling seasonal components. The findings show that the ARIMA (1,1,1)(1,0,1) model provides more accurate prediction results by accounting for seasonal fluctuations in temperature data. This research is expected to serve as a reference for preventive measures related to temperature changes, as temperature variations can affect public health, infrastructure, and quality of life in rapidly growing cities like Delhi. Understanding temperature trends and making accurate predictions helps in city planning, resource management, and developing adaptation strategies for climate change, which is crucial for mitigating negative impacts and planning for a more sustainable future.]]>
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