Hybridization of intelligent techniques and ARIMA models for time series prediction

By O. Valenzuela, Ignacio Rojas, F. Rojas, Héctor Pomares, L. Herrera, A. Guillén, L. Marquez, M. Pasadas. 2008

194 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sercan Karaoglu
2. Ugur Arpaci
3. Serkan Ayvaz
4. Abdelhamid Bouchachia

In Management Science and Operations Research

1. Sercan Karaoglu
2. Ugur Arpaci
3. Serkan Ayvaz
4. Abdelhamid Bouchachia
5. M. Ugur Gudelek
6. Omer Berat Sezer
7. A. Ozbayoglu

Citations in the corpus

• A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data (2017, methodology): “While others proposed methods based on the time series models such as the ARIMA (Autoregressive Moving Average) model [13]–[16] and the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model [17]–[19].”
• Ensemble Learning for Time Series Prediction (2008, methodology): “In [19], a combination genetic classifiers is proposed for predicting stock indexes, while in [20], a hybrid combination of neural networks and the ARIMA model is applied for time series forecasting.”