Yuan-Hai Shao
Dados Biográficos
| ID | 7544527 |
|---|---|
| NOME | Yuan-Hai Shao |
| PRENOMES | Yuan-Hai |
| SOBRENOME | Shao |
| ASSINATURA | SHAO Y |
| AFILIAÇÕES | Hainan University |
| ORCID | 0000-0002-1628-6133 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2022 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2023 |
| ÍNDICE H | 0 |
Volatility forecasting using deep neural network with time-series feature embedding
Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policy-makers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility-…
A sparse approach for high-dimensional data with heavy-tailed noise
High-dimensional data have commonly emerged in diverse fields, such as economics, finance, genetics, medicine, machine learning, and so on. In this paper, we consider the sparse quantile regression problem of high-dimensional data with heavy-tailed noise, especially when the number of regressors is much larger than the sample size. We bring the spirit of -norm support vector regression into quantile regression and propose a robust -norm support v…
Sem obras proeminentes nesta página.
A sparse approach for high-dimensional data with heavy-tailed noise
High-dimensional data have commonly emerged in diverse fields, such as economics, finance, genetics, medicine, machine learning, and so on. In this paper, we consider the sparse quantile regression problem of high-dimensional data with heavy-tailed noise, especially when the number of regressors is much larger than the sample size. We bring the spirit of -norm support vector regression into quantile regression and propose a robust -norm support v…
Volatility forecasting using deep neural network with time-series feature embedding
Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policy-makers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility-…
Artificial Intelligence (2 obras) · Computer Science (2 obras) · Econometrics (2 obras) · Mathematics (2 obras) · Artificial neural network (1 obras) · Bayesian Methods and Mixture Models (1 obras) · Embedding (1 obras) · Energy Load and Power Forecasting (1 obras) · Feature selection (1 obras) · Gene expression and cancer classification (1 obras)