Global temperature anomaly prediction by using additive twin LSTM networks

Key Points

• The model predicts global temperature anomalies effectively, improving forecasting capabilities based on historical data.
• Analysis shows a prediction accuracy rate of over 85% across multiple time intervals by employing advanced machine learning techniques.
• Observational analysis uses additive twin LSTM networks to enhance prediction reliability of temperature variations globally.
• This approach may enable better climate modeling, though further external validation is necessary.