Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Guoqi Qian"'
Publikováno v:
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 2084-2110 (2022)
Coverage, resolution, and accuracy in the spatial and temporal estimates of remotely sensed precipitation from space satellites, along with the number of instruments deployed to deliver these observations, are increasing. Of key interest in this stud
Externí odkaz:
https://doaj.org/article/c6c1e485021a4fd29bf719b39921537d
Publikováno v:
Forecasting, Vol 3, Iss 4, Pp 850-867 (2021)
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advance
Externí odkaz:
https://doaj.org/article/bb63053ea8f34362bc3cccb6d985d0d1
Publikováno v:
Engineering Proceedings, Vol 39, Iss 1, p 21 (2023)
Landslides are nonstationary and nonlinear phenomena, which are often recorded as high-dimensional vector time series manifesting spatiotemporal dependence. Contemporary econometric methods use error-correction cointegration (ECC) and vector autoregr
Externí odkaz:
https://doaj.org/article/fd5e5984aec64096a2935099b72451b3
Autor:
Pei-Yun Sun, Guoqi Qian
Publikováno v:
Engineering Proceedings, Vol 39, Iss 1, p 29 (2023)
Functional data analysis has demonstrated significant success in time series analysis. In recent biomedical research, it has also been used to analyze sequence variations in genome-wide association studies (GWAS). The observations of genetic variants
Externí odkaz:
https://doaj.org/article/91b8e896f5be43419c47d5f8b3628aaa
Publikováno v:
Econometrics, Vol 11, Iss 2, p 13 (2023)
Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies
Externí odkaz:
https://doaj.org/article/7c6259aad45a481386a67df094709ec2
Publikováno v:
Environmental Research: Climate, Vol 2, Iss 1, p 011002 (2023)
Changing climate in Australia has significant impacts on the country’s economy, environment and social well-being. Addressing such impacts, particularly that of precipitation change, entails immediate action due to the more frequent occurrence of e
Externí odkaz:
https://doaj.org/article/510b5a7499fd4840acb0fc0f6d477c93
Publikováno v:
Atmosphere, Vol 14, Iss 2, p 193 (2023)
In this paper, we study the problem of extracting trends from time series data involving missing values. In particular, we investigate a general class of procedures that impute the missing data and then extract trends using seasonal-trend decompositi
Externí odkaz:
https://doaj.org/article/ee02d54efbdf4b27985453e86b4d5710
Autor:
Chengyu Li, Guoqi Qian
Publikováno v:
Applied Sciences, Vol 13, Iss 1, p 222 (2022)
Stock price prediction is crucial but also challenging in any trading system in stock markets. Currently, family of recurrent neural networks (RNNs) have been widely used for stock prediction with many successes. However, difficulties still remain to
Externí odkaz:
https://doaj.org/article/1aeadc7ff5be4f06b9c9df40232fc33d
Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5872 (2022)
A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australi
Externí odkaz:
https://doaj.org/article/f3ff76d359d44af5b57513b76af19a73
Publikováno v:
Applied Mathematical Modelling. 110:441-454
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::752039352eaf4abcce9d97cad4a27745
https://doi.org/10.1016/j.apm.2022.05.036
https://doi.org/10.1016/j.apm.2022.05.036