Predictive Model of the ENSO Phenomenon Based on Regression Trees

Abstract

In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.

Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge

“…Research shows that successful ICOs have well-informed white papers with terms that are considered model parameters [24]. Moreover, the performance of the predictive model was validated using three continuous error measurement metrics: Mean Absolute Error (MAE), Maximum Error, and Root Mean Square Error (RMSE) [16]. These metrics are commonly used to assess the accuracy of predictive models.…”

“…In other words, as more data was used to train the model, it became more accurate in making predictions, resulting in smaller errors between the predicted values and the actual values. By considering these metrics, it can be inferred that the predictive model demonstrated better performance with increased training data, leading to improved accuracy in its forecasts [16]. The use of standard statistical parameters implies that various metrics were employed to assess the models' performance.…”