| dc.contributor.author |
Cocovilo Filho, Luis Fernando Panicachi |
|
| dc.contributor.author |
Coelho, Guilherme Palermo |
|
| dc.date |
2022-08-19 |
|
| dc.date.accessioned |
2022-07-21 |
|
| dc.date.accessioned |
2022-08-20T12:11:11Z |
|
| dc.date.available |
2022-08-20T12:11:11Z |
|
| dc.identifier.uri |
https://doi.org/10.25824/redu/WB2R60 |
|
| dc.identifier.uri |
https://redu.unicamp.br/dataset.xhtml?persistentId=doi:10.25824/redu/WB2R60 |
|
| dc.description |
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> |
|
| dc.description.sponsorship |
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior |
|
| dc.format |
application/octet-stream |
|
| dc.format |
application/octet-stream |
|
| dc.format |
application/octet-stream |
|
| dc.format |
application/octet-stream |
|
| dc.format |
application/octet-stream |
|
| dc.publisher |
Coelho, Guilherme Palermo |
|
| dc.subject |
Computer and Information Science |
|
| dc.title |
Replication data for: evaluation of concept drift detection approaches for stock market forecasting |
|
| dc.description.sponsorshipId |
CAPES: 001 |
|