Time Series Classification on PEMS

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	FCN-SNLST	0.857	No	Seq2Tens: An Efficient Representation of Sequenc...	2020-06-12	Code
2	GP-Sig	0.82	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code
3	GP-KConv1D	0.794	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code
4	GP-Sig-GRU	0.775	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code
5	GP-GRU	0.769	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code
6	GP-Sig-LSTM	0.763	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code
7	SNLST	0.747	No	Seq2Tens: An Efficient Representation of Sequenc...	2020-06-12	Code
8	GP-LSTM	0.745	No	Bayesian Learning from Sequential Data using Gau...	2019-06-19	Code

#1FCN-SNLSTSOTA
0.857
Accuracy· 2020-06-12
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections Code
#2GP-SigSOTA
0.82
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code
#3GP-KConv1D
0.794
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code
#4GP-Sig-GRU
0.775
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code
#5GP-GRU
0.769
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code
#6GP-Sig-LSTM
0.763
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code
#7SNLST
0.747
Accuracy· 2020-06-12
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections Code
#8GP-LSTM
0.745
Accuracy· 2019-06-19
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Code