A Comparative Study of ARIMA and LSTM Models for Short-Term Forecasting of the El Nino Modoki Index

Abstract

To get a handle on global climate patterns, we need to accurately forecast the El Niño Modoki Index (EMI). This study compares two methods for predicting the EMI's short-term behaviour based solely on its historical data. We pitted a classic statistical method, the AutoRegressive Integrated Moving Average (ARIMA) model, against a modern deep learning approach using a Long Short-Term Memory (LSTM) network. We trained both models on monthly EMI data from 1982 to 2022 and then tested how well they could predict the period from 2023 to 2025.
Our results showed that the ARIMA(2,0,0) model works as a solid, understandable baseline, capturing the main movements of the index with a Root Mean Squared Error (RMSE) of 0.4338 and an R-squared (R²) of only 0.0533. The LSTM network, however, was much better at handling the quirky, non-linear nature of the data, leading to a far more accurate forecast with an RMSE of just 0.0820 and an R² of 0.9658. Ultimately, while a simple ARIMA model is useful as a benchmark, our work makes it clear that LSTM networks can offer a major leap forward in forecasting accuracy for complex climate indicators like the EMI.