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1. Overview:

   • Analysis of global temperature anomalies (1880-2024).
   • Utilises ARIMA and SARIMA models to explore past patterns and forecast future trends.
   • Highlights the impact of industrialisation, conflicts, and policies on temperature changes.
   • Forecasts suggest ongoing warming without significant intervention.

2. Data Collection and Cleaning:

   • Dataset from "Tables of Global and Hemispheric Anomalies" containing monthly anomalies.
   • Missing values filled using interpolation, preserving natural variability.
   • Data standardised for improved model performance.

3. Time Series Decomposition and Differencing:

   • Time series decomposition into trend, seasonal, and residual components.
   • Differencing applied to stabilise the mean and eliminate trends, enhancing model accuracy.

4. ACF and PACF Analysis Before and After Differencing:

   • Before differencing: Non-stationary series with persistent trends.
   • After differencing: Stationary series with reduced long-term dependencies, suitable for ARIMA/SARIMA models.

5. Monthly Temperature Anomalies:

   • Significant warming across all months, particularly in June and December.
   • Indicates intensifying summer heat and milder winters.

6. Seasonal Temperature Trends:

   • Notable warming in winter and summer months.
   • Winter warming linked to Arctic amplification, affecting snow cover and ice.
   • Summer warming may lead to more extreme heatwaves and droughts.

7. Correlation with Historical Events:

   • Key periods: Industrial Revolution, World Wars, Post-War Boom, Environmental Policies.
   • Historical events show a strong correlation with rising temperatures due to increased emissions.

8. Forecasting Future Trends:

   • SARIMA and ARIMA models forecast continued global temperature rise.
   • Projections emphasise the need for urgent climate action to avoid more severe weather events and ecological disruptions.

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