#graph neural network #graph #forecasting #time series

stateDiagram-v2 with_spectral_matrix_representation: Spectral Matrix Representation with_temporal_patterns_in_freq_domain: Temporal Patterns in Frequency Domain with_latent_correlation: Latent Correlation Representation dft_repr: Discrete Fourier Transformed Representation conv_dft_repr: Convolved DFT Representation [*] --> with_latent_correlation: Latent Correlation Layer with_latent_correlation --> with_latent_correlation state with_latent_correlation { state with_spectral_matrix_representation { [*] --> dft_repr: Discrete Fourier Transform dft_repr --> conv_dft_repr: 1DConv conv_dft_repr --> gated_conv_dft_repr: Gated Linear Unit gated_conv_dft_repr --> inverse_dft_gated_conv_dft_repr: IDFT } [*] --> with_spectral_matrix_representation: Graph Fourier Transform note right of with_spectral_matrix_representation: This happens in Spe-Seq Cell with_spectral_matrix_representation --> with_temporal_patterns_in_freq_domain with_temporal_patterns_in_freq_domain --> with_conv_temporal_patterns: Graph Convolution with_conv_temporal_patterns --> with_timeseries_restored: IGFT with_timeseries_restored --> Forecast with_timeseries_restored --> Backcast } note right of with_latent_correlation: This is done in StemGNN Block with_latent_correlation --> [*]

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Lei Ma (2022). 'StemGNN', Datumorphism, 01 April. Available at:

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