LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

Related tags

Deep Learningmachine-learningdeep-learningtime-seriespytorchtransformerforecastingself-attention
Overview

Query Selector

Here you can find code and data loaders for the paper https://arxiv.org/pdf/2107.08687v1.pdf . Query Selector is a novel approach to sparse attention Transformer algorithm that is especially suitable for long term time series forecasting

Depencency

Python            3.7.9
deepspeed         0.4.0
numpy             1.20.3
pandas            1.2.4
scipy             1.6.3
tensorboardX      1.8
torch             1.7.1
torchaudio        0.7.2
torchvision       0.8.2
tqdm              4.61.0

Results on ETT dataset

Univariate

Data Prediction len Informer MSE Informer MAE Trans former MSE Trans former MAE Query Selector MSE Query Selector MAE MSE ratio
ETTh1 24 0.0980 0.2470 0.0548 0.1830 0.0436 0.1616 0.445
ETTh1 48 0.1580 0.3190 0.0740 0.2144 0.0721 0.2118 0.456
ETTh1 168 0.1830 0.3460 0.1049 0.2539 0.0935 0.2371 0.511
ETTh1 336 0.2220 0.3870 0.1541 0.3201 0.1267 0.2844 0.571
ETTh1 720 0.2690 0.4350 0.2501 0.4213 0.2136 0.3730 0.794
ETTh2 24 0.0930 0.2400 0.0999 0.2479 0.0843 0.2239 0.906
ETTh2 48 0.1550 0.3140 0.1218 0.2763 0.1117 0.2622 0.721
ETTh2 168 0.2320 0.3890 0.1974 0.3547 0.1753 0.3322 0.756
ETTh2 336 0.2630 0.4170 0.2191 0.3805 0.2088 0.3710 0.794
ETTh2 720 0.2770 0.4310 0.2853 0.4340 0.2585 0.4130 0.933
ETTm1 24 0.0300 0.1370 0.0143 0.0894 0.0139 0.0870 0.463
ETTm1 48 0.0690 0.2030 0.0328 0.1388 0.0342 0.1408 0.475
ETTm1 96 0.1940 0.2030 0.0695 0.2085 0.0702 0.2100 0.358
ETTm1 288 0.4010 0.5540 0.1316 0.2948 0.1548 0.3240 0.328
ETTm1 672 0.5120 0.6440 0.1728 0.3437 0.1735 0.3427 0.338

Multivariate

Data Prediction len Informer MSE Informer MAE Trans former MSE Trans former MAE Query Selector MSE Query Selector MAE MSE ratio
ETTh1 24 0.5770 0.5490 0.4496 0.4788 0.4226 0.4627 0.732
ETTh1 48 0.6850 0.6250 0.4668 0.4968 0.4581 0.4878 0.669
ETTh1 168 0.9310 0.7520 0.7146 0.6325 0.6835 0.6088 0.734
ETTh1 336 1.1280 0.8730 0.8321 0.7041 0.8503 0.7039 0.738
ETTh1 720 1.2150 0.8960 1.1080 0.8399 1.1150 0.8428 0.912
ETTh2 24 0.7200 0.6650 0.4237 0.5013 0.4124 0.4864 0.573
ETTh2 48 1.4570 1.0010 1.5220 0.9488 1.4074 0.9317 0.966
ETTh2 168 3.4890 1.5150 1.6225 0.9726 1.7385 1.0125 0.465
ETTh2 336 2.7230 1.3400 2.6617 1.2189 2.3168 1.1859 0.851
ETTh2 720 3.4670 1.4730 3.1805 1.3668 3.0664 1.3084 0.884
ETTm1 24 0.3230 0.3690 0.3150 0.3886 0.3351 0.3875 0.975
ETTm1 48 0.4940 0.5030 0.4454 0.4620 0.4726 0.4702 0.902
ETTm1 96 0.6780 0.6140 0.4641 0.4823 0.4543 0.4831 0.670
ETTm1 288 1.0560 0.7860 0.6814 0.6312 0.6185 0.5991 0.586
ETTm1 672 1.1920 0.9260 1.1365 0.8572 1.1273 0.8412 0.946

State Of Art

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Citation

@misc{klimek2021longterm,
      title={Long-term series forecasting with Query Selector -- efficient model of sparse attention}, 
      author={Jacek Klimek and Jakub Klimek and Witold Kraskiewicz and Mateusz Topolewski},
      year={2021},
      eprint={2107.08687},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contact

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Owner
MORAI
MORAI
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