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Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model

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Academic year: 2021

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Fig. 2: Inferred transition matrices for different test samples, using VAR, NRI, Seq2VAR and binary Seq2VAR, compared to ground truth, depending on the level of observation noise: N (0, 0.1) (left), N (0, 0.3) (middle), N (0, 0.5) (right).
Fig. 3: Histogram of the distribution of the entries of inferred transition matrices over a test set, using VAR, NRI, Seq2VAR and binary Seq2VAR, compared to ground truth, depending on the level of observation noise: N (0, 0.1) (left), N (0, 0.5) (right).
Fig. 4: Quartiles of the distribution of the L 1 distances between true and inferred causality graphs with VAR, NRI, Seq2VAR and binary Seq2VAR respectively with observation noise N (0, 0.1) (left), N (0, 0.3) (middle) and N (0, 0.5) (right).
Fig. 5: Example of 3-ball-springs system (left) and its Granger causality graph (right)
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