Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Kaijian He"'
Publikováno v:
Mathematics, Vol 11, Iss 14, p 3187 (2023)
With the emergence of big data and the resulting information explosion, computational and mathematical methods provide effective tools to handle the vast amounts of data and information used in big data analytics, knowledge discovery and distillation
Externí odkaz:
https://doaj.org/article/36f8cfa7f88a4db193fa1d508b388126
Publikováno v:
Mathematics, Vol 11, Iss 4, p 1054 (2023)
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operati
Externí odkaz:
https://doaj.org/article/e423c13c23434c4abf0ef8174f5f1692
Publikováno v:
Mathematics, Vol 10, Iss 16, p 2999 (2022)
The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new m
Externí odkaz:
https://doaj.org/article/13054bdfb30648ae9f531444f43d0fd6
Autor:
Yingchao Zou, Kaijian He
Publikováno v:
Mathematics, Vol 10, Iss 14, p 2413 (2022)
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network mod
Externí odkaz:
https://doaj.org/article/e0fa24e4663e419f96d934d0450ec5a1
Publikováno v:
Sustainable Futures, Vol 2, Iss , Pp 100003- (2020)
Analyses of financial time series and exploring its underlying characteristic factors are longstanding research problems. Ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA) are two methods developed to deal with the
Externí odkaz:
https://doaj.org/article/20026975f870446b953fa9968248131a
Publikováno v:
Entropy, Vol 17, Iss 10, Pp 7167-7184 (2015)
For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families a
Externí odkaz:
https://doaj.org/article/988a0ab8cf53411daf007829dbb10272
Publikováno v:
Entropy, Vol 17, Iss 7, Pp 4519-4532 (2015)
In this paper, we propose a new entropy-optimized bivariate empirical mode decomposition (BEMD)-based model for estimating portfolio value at risk (PVaR). It reveals and analyzes different components of the price fluctuation. These components are dec
Externí odkaz:
https://doaj.org/article/76d280c6b78d41c8a9a440f5b9f79d1d
Publikováno v:
Energies, Vol 5, Iss 4, Pp 1018-1043 (2012)
In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typic
Externí odkaz:
https://doaj.org/article/34f46df2f8f640a8bebf9a883321c7c1
Publikováno v:
The Scientific World Journal, Vol 2014 (2014)
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics
Externí odkaz:
https://doaj.org/article/6a37a9ce483c497791a2f44a79fc028e
Publikováno v:
Energies, Vol 9, Iss 11, p 931 (2016)
The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement model
Externí odkaz:
https://doaj.org/article/e7a7af2de5c0499793a9816017684857