Results 31 to 40 of about 45,363 (158)
Graph Neural Network for Traffic Flow Situation Prediction
Jisuanji kexue yu tansuo, 2021 Road network structure integrated traffic flow situation prediction is a highly nonlinear and complexly spatial-temporal dynamic correlation time-series data prediction problem. However, traditional traffic flow situation forecasting methods cannot model
JIANG Shan, DING Zhiming, XU Xinrun, YAN Jin
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On Approximation of Convolutions by Accompanying Laws in the Scheme of Series [PDF]
Journal of Mathematical Sciences, 2014 The problem of the approximation of convolutions by accompanying laws in the scheme of series satisfying the infinitesimality condition is considered. It is shown that the quality of approximation depends essentially on the choice of centering constants.
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Restrictions of Eisenstein Series and Rankin-Selberg Convolution
Documenta Mathematica, 2019 In a 2005 paper, Yang constructed families of Hilbert Eisenstein series, which when restricted to the diagonal are conjectured to span the underlying space of elliptic modular forms. One approach to these conjectures is to show the non-vanishing of an inner product of elliptic eigenforms with the restrictions of Eisenstein series.
Keaton, Rodney, Pitale, Ameya
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Demonstratio Mathematica, 2018 Recently the authors obtained several Laplace transforms of convolution type integrals involving Kummer’s function 1F1 [Appl. Anal. Discrete Math., 2018, 12(1), 257-272].
Milovanović Gradimir V. +2 more
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Wind speed prediction based on multi-channel long short-term memory convolution neural network
电力工程技术, 2022 A wind speed prediction method based on the combination of multi-channel long short-term memory network and convolution neural network is proposed to improve the prediction performance of wind speed.
XIU Chunbo, SU Huan, SU Xuemiao
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Mathematics, 2020 In this work, properties of one- or two-parameter Mittag-Leffler functions are derived using the Laplace transform approach. It is demonstrated that manipulations with the pair direct–inverse transform makes it far more easy than previous methods to ...
Alexander Apelblat
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Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
Future Internet, 2022 It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in ...
Yuefeng Cen +4 more
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Time Series Encodings with Temporal Convolutional Networks
, 2020 The training of anomaly detection models usually requires labeled data. We present in this paper a novel approach for anomaly detection in time series which trains unsupervised using a convolutional approach coupled to an autoencoder framework. After training, only a small amount of labeled data is needed to adjust the anomaly threshold.
Markus Thill +2 more
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Applied SciencesAccurate prediction of traffic flow is the key to highways control. However, traditional time series forecasting methods cannot meet the accuracy requirements of long-term forecasting.
Meng Yang +3 more
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A convolution structure for Laguerre series
Indagationes Mathematicae (Proceedings), 1982 AbstractWatson's product formula for Laguerre polynomials has been used by McCully [12] and Askey [1] to define a convolution structure. In comparison with the Jacobi expansion, the results in the Laguerre case are less satisfactory since the Laguerre translation operator is not positive. Nevertheless, other choices of the underlying spaces may lead to
Görlich, E., Markett, C.
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