Forecasting stochastic neural network based on financial empirical mode decomposition

By Jie Wang, Jun Wang. 2017

95 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, 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 Electrical and Electronic Engineering

1. Rob J Hyndman
2. Spyros Makridakis
3. Fotios Petropoulos
4. Tao Hong
5. Han Lin Shang
6. Rafal Weron

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Spyros Makridakis
4. Konstantinos Nikolopoulos
5. Vassilios Assimakopoulos
6. Evangelos Spiliotis
7. George Athanasopoulos
8. Nikolaos Kourentzes

In Signal Processing

1. Spyros Makridakis
2. Fotios Petropoulos
3. Rob J Hyndman
4. Vassilios Assimakopoulos
5. Konstantinos Nikolopoulos
6. Evangelos Spiliotis
7. George Athanasopoulos
8. Yanfei Kang

Citations in the corpus

• Forecasting in social settings: The state of the art (2020, background): “For stock-market data, for example, it is essential to include a naive benchmark, yet this is often not done (see, for example, Wang and Wang, 2017).”