Zobrazeno 1 - 10
of 5 485
pro vyhledávání: '"Multivariate Time Series"'
Publikováno v:
Information Processing in Agriculture, Vol 11, Iss 4, Pp 542-551 (2024)
Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completel
Externí odkaz:
https://doaj.org/article/955a41785ba7455cbc10c82d893dd724
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-14 (2024)
Abstract The particulate matter (PM)2.5 forecasting has been being advanced with the development of deep learning methods. However, most of them do not consider the active population exposed to air pollution. We propose to apply a population-based ce
Externí odkaz:
https://doaj.org/article/a343488d4ffc46f0b33c3792eecfa5e6
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract In recent years, the uncertainty of weather conditions and the impact of future climate change on building energy assessment has received increasing attention. As an important part of these studies, several types of methods for generating st
Externí odkaz:
https://doaj.org/article/f63c831cfcca4da592de64586eccfffb
Autor:
Jie Yu, Huimin Wang, Miaoshuang Chen, Xinyue Han, Qiao Deng, Chen Yang, Wenhui Zhu, Yue Ma, Fei Yin, Yang Weng, Changhong Yang, Tao Zhang
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-16 (2024)
Abstract Background Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional i
Externí odkaz:
https://doaj.org/article/cffd13fea3864a8e865225bc1811ea12
Autor:
Bubryur Kim, K. R. Sri Preethaa, Sujeen Song, R. R. Lukacs, Jinwoo An, Zengshun Chen, Euijung An, Sungho Kim
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-37 (2024)
Abstract The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To
Externí odkaz:
https://doaj.org/article/81c64d9d4c39449cae3d17a37e5c47e4
Autor:
Abhidnya Patharkar, Jiajing Huang, Teresa Wu, Erica Forzani, Leslie Thomas, Marylaura Lind, Naomi Gades
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative m
Externí odkaz:
https://doaj.org/article/3ab533fc6a494db5b12d44d5ce6d7330
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount
Externí odkaz:
https://doaj.org/article/06f21d1da2ea47d99712f90598630c80
Publikováno v:
Earth and Space Science, Vol 11, Iss 10, Pp n/a-n/a (2024)
Abstract Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite‐observed period from 1979 to 2022 the sea ice extent has continuously
Externí odkaz:
https://doaj.org/article/f570c52cfdec4c1f97f50fecad941021
Publikováno v:
工程科学学报, Vol 46, Iss 6, Pp 1108-1119 (2024)
The blowing process in converter steelmaking at the blowing stage mainly includes oxygen supply, slag discharge, and bottom blowing. The stability of the blowing process directly affects the quality of the molten steel at the end. The traditional sta
Externí odkaz:
https://doaj.org/article/cdfa8668f4a8492a974dd904105db4ab
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-14 (2024)
Abstract The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allow
Externí odkaz:
https://doaj.org/article/b099cfd7fb14457d9b04d0a5f6819763