Extra Trees Method for Stock Price Forecasting With Rolling Origin Accuracy Evaluation

Abstract

Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…After training the trees, the algorithm makes its final prediction based on test data through majority voting for classification or by calculating the arithmetic mean for regression [38]. The Extra Trees or Extremely Randomized Trees model is widely used in various fields, including health [39,129,132], economy [38], transportation [129], and telecommunications [41][42][43]. For instance, in medicine, the Extra Trees model has been proposed to detect and classify cancer as malignant or benign tumors.…”

“…This article proposes a new classifier Extra Trees (ETC) and its algorithm for the rapid diagnosis of faults in photovoltaic systems. As demonstrated in various publications across different fields, including economics [38], medicine [39,40], hydraulic engineering, and telecommunications [41][42][43], the Extra Trees algorithm has shown robustness to noise, a significant reduction in bias errors, and lower variance compared to other models such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Decision Trees (DT) [39]. Furthermore, this algorithm exhibits a lower computational complexity rate compared to other Machine Learning (ML) classification models, such as DT, Adaptive Boosting (AdaBoost), Naïve Bayes (NB), SVM, RF and KNN [39].…”