IDPSO-ELM-S: Time series forecasting in the presence of concept drift, A PSO-based approach -

The module Main.py executes the algorithms described below in real and synthetic time series.

Usage

# Cloning the repository
git clone https://github.com/GustavoHFMO/IDPSO-ELM-S.git

# Acessing the repository
cd IDPSO-ELM-S

# Installing the dependencies
pip install -r requirements.txt

# Running the code
python Main.py

IDPSO-ELM-S:

OLIVEIRA, Gustavo HFMO et al. Time series forecasting in the presence of concept drift: A pso-based approach. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. p. 239-246.

IDPSO-ELM-B:

OLIVEIRA, Gustavo HFMO et al. Time series forecasting in the presence of concept drift: A pso-based approach. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. p. 239-246.

ELM-FEDD:

R. C. Cavalcante, L. L. Minku, and A. L. Oliveira, “FEDD: Feature Extraction for Explicit Concept Drift Detection in time series,” in Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016, pp. 740–747.

ELM-ECDD:

R. C. Cavalcante and A. L. Oliveira, “An approach to handle concept drift in financial time series based on extreme learning machines and explicit drift detection,” in Neural Networks (IJCNN), 2015 International Joint Conference on. IEEE, 2015, pp. 1–8.

ELM-DDM:

R. C. Cavalcante and A. L. Oliveira, “An approach to handle concept drift in financial time series based on extreme learning machines and explicit drift detection,” in Neural Networks (IJCNN), 2015 International Joint Conference on. IEEE, 2015, pp. 1–8.

Result

License

This project is under a GNU General Public License (GPL) Version 3. See LICENSE for more information.