Replication data for: evaluation of concept drift detection approaches for stock market forecasting

File "database.zip" contains, for each stock, (i) the original dataset, (ii) the preprocessed dataset (incremental and non-incremental preprocessing), and (iii) the dataset without normalization.<\li> File "classic_algorithms.zip" 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. File "grid_search_classic_algorithms.zip" contains the source code for hyperparameter tuning of the classical machine learning algorithms. File "algorithms_with_drift_detection.zip" 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.