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Modeling time series by aggregating multiple fuzzy cognitive maps [PDF]
PeerJ Computer Science, 2021 Background The real time series is affected by various combinations of influences, consequently, it has a variety of variation modality. It is hard to reflect the variation characteristic of the time series accurately when simulating time series only by ...
Tianming Yu, Qunfeng Gan, Guoliang Feng
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Frontiers in Immunology, 2022 The SpTransformer (SpTrf) gene family encodes a set of proteins that function in the sea urchin immune system. The gene sequences have a series of internal repeats in a mosaic pattern that is characteristic of this family.
Megan A. Barela Hudgell +1 more
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Frontiers in Energy Research, 2023 Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement ...
Mao Yang +4 more
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Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network
Mathematics, 2023 Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple ...
Zhiyuan Zhang, Zhanshan Wang
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Empirical adaptive wavelet decomposition (EAWD): an adaptive decomposition for the variability analysis of observation time series in atmospheric science [PDF]
Nonlinear Processes in Geophysics, 2022 Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several timescales and space scales. In consequence, measurement time series often have characteristics of non-linearity
O. Delage +6 more
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
Journal of Statistical Software, 2019 Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are
Satu Helske, Jouni Helske
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Journal of Advanced Transportation, 2022 Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of
Shanchun Zhao, Xu Li
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Multineuronal Spike Sequences Repeat with Millisecond Precision
Frontiers in Neural Circuits, 2013 Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space.
Koki eMatsumoto +3 more
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ClickClust: An R Package for Model-Based Clustering of Categorical Sequences
Journal of Statistical Software, 2016 The R package ClickClust is a new piece of software devoted to finite mixture modeling and model-based clustering of categorical sequences. As a special kind of time series, categorical sequences, also known as categorical time series, exhibit a time ...
Volodymyr Melnykov
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ON \(\Lambda\)-CONVERGENCE ALMOST EVERYWHERE OF MULTIPLE TRIGONOMETRIC FOURIER SERIES
Ural Mathematical Journal, 2017 We consider one type of convergence of multiple trigonometric Fourier series intermediate between the convergence over cubes and the \(\lambda \)-convergence for \(\lambda >1\).
Nikolai Yu. Antonov
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