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A deep multivariate time series multistep forecasting network
Applied Intelligence, 2021Due to that multivariate time series, multistep forecasting technology has a guiding role in many fields, such as electricity consumption, traffic flow detection, and stock price prediction, many approaches have been proposed, seeking to realize accurate prediction based on historical data.
Chenrui Yin, Qun Dai
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A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2007Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts.
Kun-Huang Huarng +2 more
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Multivariable time series forecasting using model fusion
Information Sciences, 2022Abstract The forecasting of time series provides great convenience in our daily life. Studies of time series forecasting have been used in many fields such as financial models, weather, and traffic patterns. In this paper, we propose a model fusion-based time series forecasting to improve the forecasting accuracy and efficiency.
Ruijin Wang +6 more
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Experimental Study of Multivariate Time Series Forecasting Models
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019Multivariate time series forecasting has wide applications such as traffic flow prediction, supermarket commodity demand forecasting and etc. In literature, Due to the complex temporal patterns and inter-dependencies among multivariate time series, a large number of forecasting models have been developed.
Jiaming Yin +7 more
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Large Multivariate Time Series Forecasting
2019Research on the analysis of time series has gained momentum in recent years, as knowledge derived from time series analysis can improve the decision-making process for industrial and scientific fields. Furthermore, time series analysis is often an essential part of business intelligence systems.
Hmamouche, Youssef +4 more
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Forecasting traffic time series with multivariate predicting method
Applied Mathematics and Computation, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi Yin, Pengjian Shang
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Multivariate Time Series Analysis and Forecast
1982It is well known that the multivariate computer-oriented methods of mathematical statistics are based on independent vector variables essentially. This is why the authors have been concerned, for a decade already, in the elaboration of procedures which could be considered as ”dynamized” variants of the principal component analysis or, in general, the ...
György Bánkövi +2 more
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Sequence Attention for Multivariate Time Series Forecasting
2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), 2021Wenrui Wu +5 more
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Forecasting multivariate time series
International Journal of Forecasting, 2015George Athanasopoulos, Farshid Vahid
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Online Adaptive Multivariate Time Series Forecasting
2023Amal Saadallah +2 more
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