The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets
By Hossein Hassani, Andreia Dionísio, Mansoureh Ghodsi. 2010
95 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/fdf0296b4bbf045bf57f8d4a58da698405c321b0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Hossein Hassani
- Anatoly A. Zhigljavsky
- Saeed Heravi
- K. Patterson
- Vincent W. L. Tam
- Zhixi Li
- Emmanuel Sirimal Silva
- Rahim Mahmoudvand
In Computer Vision and Pattern Recognition
In Economics and Econometrics
- Anatoly A. Zhigljavsky
- Saeed Heravi
- K. Patterson
- Vincent W. L. Tam
- Zhixi Li
- Hossein Hassani
- Weiwei Jiang
Citations in the corpus
- Multivariate singular spectrum analysis for forecasting revisions to real-time data (2011, methodology): “[39,40] show that the results obtained by the SSA method are more accurate than those obtained by the ARIMA and generalised autoregressive conditional heteroscedasticity models.”
- A review on singular spectrum analysis for economic and financial time series (2010, unknown): “Keywords and phrases: Singular spectrum analysis, Cointegration, Economic/Finacial Time Series, Filtering, Forecasting, Smoothing, Unit root, Causality.”
- Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes (2017, methodology): “The simplest way for denoising data is to use a moving average method [1].”