This package contains the datasets, source codes (in Python), and complete tables with the results reported in the work entitled <a href="https://hdl.handle.net/20.500.12733/5163" target="_blank">Evaluation of Concept Drift Detection Approaches for Stock Market Forecasting</a>.<br> <ul> <li>File "<em>database.zip</em>" contains, for each stock, (i) the original dataset, (ii) the preprocessed dataset (incremental and non-incremental preprocessing), and (iii) the dataset without normalization.<\li> <li>File "<em>classic_algorithms.zip</em>" contains the source codes of the K-Nearest Neighbors (KNN), Random Forest (RF), Adjusted Random Forest (RF-A), and Support Vector Machines (SVM) models used in the experiments. This file also contains the experimental results of each model, in text files.</li> <li>File "<em>grid_search_classic_algorithms.zip</em>" contains the source code for hyperparameter tuning of the classical machine learning algorithms.</li> <li>File "<em>algorithms_with_drift_detection.zip</em>" contains the source codes (both for hyperparameter tuning and experimental runs) of the Online Sequential Extreme Learning Machines (OS-ELM), Dynamic and Online Ensemble for Regression (DOER), DOER with component ranking (DOER-Rank), Ensemble of Online Learners With Substitution of Models (EOS), and EOS with weighted average (EOS-D) models. These models were implemented according to [1].</li> <li>Finally, the file "<em>results.zip</em>" contains a spreadsheet with the detailed results of the performed experiments. </li> </ul> <br> [1] BUENO BARAJAS, Jorge Andrés. <strong>Dynamic ensemble mechanisms to improve particulate matter forecasting</strong>: Mecanismos para ensemble dinâmicos aplicados para a previsão de material particulado. 2018. 1 recurso online (91 p.) Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia, Limeira, SP. DOI: <a href="https://doi.org/10.47749/T/UNICAMP.2018.1031495" target="_blank">10.47749/T/UNICAMP.2018.1031495<a>