Layered Ensemble Architecture for Time Series Forecasting

By Md. Mustafizur Rahman, M. Islam, Kazuyuki Murase, Xin Yao. 2016

38 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing

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. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl

In Management Science and Operations Research

1. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl
4. Hansika Hewamalage
5. Stefan Zohren
6. Bryan Lim

In Signal Processing

1. Christoph Bergmeir
2. Kasun Bandara
3. Slawek Smyl

Citations in the corpus

• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “networks, generalized regression neural networks and ensemble architectures to uplift the constraints of the conventional NN architecture (Nelson et al., 1999; Zhang and Qi, 2005; Ilies et al., 2007; Rahman et al., 2016; Yan, 2012; Zimmermann et al., 2012). However, in the time series forecasting co”
• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “Many variations of ensemble architectures have been employed in time series forecasting, such as model stacking (Rahman et al., 2016; Crone et al., 2011), bagging and boosting (Barrow and Crone, 2016; Kourentzes et al.”