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A Recurrence Plot-Based Graph Convolutional Network for Time Series Classification

https://doi.org/10.2139/ssrn.4829471
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Fully convolutional networks with shapelet features for time series classification
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A new multi-process collaborative architecture for time series classification
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Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
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Recurrences determine the dynamics
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Recurrence networks—a novel paradigm for nonlinear time series analysis
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Spatial-temporal Graph Attention Networks for Traffic Flow Forecasting
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LSTM Fully Convolutional Networks for Time Series Classification
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Classification of Cognitive Evoked Potentials for ADHD Detection in Children using Recurrence Plots and CNNs
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Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks
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Classification of time-series images using deep convolutional neural networks
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Time series classification based on statistical features
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Recurrence Plots of Dynamical Systems
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Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
inferredcites →https://doi.org/10.14569/ijacsa.2019.0100582openalex-extracted
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Topological Data Analysis as a New Tool for EEG Processing
inferredcites →https://doi.org/10.3390/app12178685openalex-extracted
Time Series Classification with Shapelet and Canonical Features
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Multi-Scale Convolutional Neural Networks for Time Series Classification
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Semi-Supervised Classification with Graph Convolutional Networks
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Classification of Time-Series Images Using Deep Convolutional Neural Networks
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