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sclformer's Introduction

SCLformer

SCLformer for LSTF problem

Introduce

SCLformer is short for Self-adaptive Convolution Linear Transformer. It is a transformer-based model for LSTF problem. The model is based on the paper. The model is implemented by Pytorch.

Usage

Dataset

Dataset can be downloaded from here.

Result

The result of SCLformer is shown as follows:

Multivariable condition:

Methods SCLformer Informer LSTM ARMA DeepAR
Metric MSE MAE MSE MAE MSE MAE MSE MAE MSE MAE
ETTh1 96 0.903 0.73 0.906 0.746 1.201 0.861 0.846 0.607 0.833 0.701
192 1.024 0.797 0.843 0.694 1.23 0.831 0.861 0.62 0.939 0.749
336 0.999 0.785 1.157 0.849 1.292 0.921 0.875 0.635 1.088 0.785
720 1.065 0.835 1.231 0.887 1.126 0.839 0.884 0.653 1.078 0.846
ETTh2 96 1.767 1.097 2.723 1.404 2.193 1.179 3.154 1.353 2.283 1.196
192 3.516 1.605 5.904 2.11 3.236 1.402 3.166 1.357 3.355 1.565
336 3.56 1.586 3.511 1.553 2.533 1.274 3.149 1.351 2.681 1.405
720 4.232 1.762 3.283 1.56 3.498 1.552 3.113 1.342 3.049 1.333
ETTm1 96 0.655 0.585 0.556 0.529 1.124 0.8222 0.865 0.619 0.779 0.701
192 0.697 0.631 0.872 0.69 1.261 0.898 0.871 0.621 0.805 0.712
336 0.865 0.715 0.933 0.739 1.14 0.843 0.882 0.631 0.83 0.724
720 0.927 0.751 0.918 0.732 1.184 0.869 0.899 0.644 0.889 0.75
ETTm2 96 0.696 0.607 0.539 0.522 1.134 0.887 3.121 1.343 1.026 0.816
192 1.083 0.753 1.05 0.769 2.366 1.299 3.128 1.346 1.498 0.952
336 0.885 0.726 1.451 0.935 2.612 1.321 3.142 1.349 1.997 1.136
720 1.835 1.042 2.441 1.172 3.127 1.508 3.152 1.352 2.368 1.212
Weather 96 0.755 0.665 0.488 0.497 0.554 0.548 0.618 0.557 0.487 0.5
192 0.795 0.687 0.581 0.552 0.604 0.581 0.642 0.579 0.528 0.532
336 0.676 0.613 0.604 0.571 0.617 0.589 0.647 0.588 0.546 0.547
720 0.746 0.648 0.614 0.583 0.652 0.61 0.672 0.61 0.638 0.603
ECL 96 0.336 0.404 0.303 0.395 1.013 0.837 0.584 0.572 0.53 0.541
192 0.311 0.39 0.291 0.378 0.932 0.793 0.582 0.574 0.492 0.522
336 0.306 0.387 0.302 0.392 0.962 0.801 0.586 0.582 0.488 0.522
720 0.314 0.395 0.381 0.447 0.953 0.804 0.603 0.599 0.505 0.514

Singlevariable condition:

Methods SCLformer Informer LSTM ARMA DeepAR
Metric MSE MAE MSE MAE MSE MAE MSE MAE MSE MAE
ETTh1 96 0.903 0.73 0.906 0.746 1.201 0.861 0.846 0.607 0.833 0.701
192 1.024 0.797 0.843 0.694 1.23 0.831 0.861 0.62 0.939 0.749
336 0.999 0.785 1.157 0.849 1.292 0.921 0.875 0.635 1.088 0.785
720 1.065 0.835 1.231 0.887 1.126 0.839 0.884 0.653 1.078 0.846
ETTh2 96 1.767 1.097 2.723 1.404 2.193 1.179 3.154 1.353 2.283 1.196
192 3.516 1.605 5.904 2.11 3.236 1.402 3.166 1.357 3.355 1.565
336 3.56 1.586 3.511 1.553 2.533 1.274 3.149 1.351 2.681 1.405
720 4.232 1.762 3.283 1.56 3.498 1.552 3.113 1.342 3.049 1.333
ETTm1 96 0.655 0.585 0.556 0.529 1.124 0.8222 0.865 0.619 0.779 0.701
192 0.697 0.631 0.872 0.69 1.261 0.898 0.871 0.621 0.805 0.712
336 0.865 0.715 0.933 0.739 1.14 0.843 0.882 0.631 0.83 0.724
720 0.927 0.751 0.918 0.732 1.184 0.869 0.899 0.644 0.889 0.75
ETTm2 96 0.696 0.607 0.539 0.522 1.134 0.887 3.121 1.343 1.026 0.816
192 1.083 0.753 1.05 0.769 2.366 1.299 3.128 1.346 1.498 0.952
336 0.885 0.726 1.451 0.935 2.612 1.321 3.142 1.349 1.997 1.136
720 1.835 1.042 2.441 1.172 3.127 1.508 3.152 1.352 2.368 1.212
Weather 96 0.755 0.665 0.488 0.497 0.554 0.548 0.618 0.557 0.487 0.5
192 0.795 0.687 0.581 0.552 0.604 0.581 0.642 0.579 0.528 0.532
336 0.676 0.613 0.604 0.571 0.617 0.589 0.647 0.588 0.546 0.547
720 0.746 0.648 0.614 0.583 0.652 0.61 0.672 0.61 0.638 0.603
ECL 96 0.336 0.404 0.303 0.395 1.013 0.837 0.584 0.572 0.53 0.541
192 0.311 0.39 0.291 0.378 0.932 0.793 0.582 0.574 0.492 0.522
336 0.306 0.387 0.302 0.392 0.962 0.801 0.586 0.582 0.488 0.522
720 0.314 0.395 0.381 0.447 0.953 0.804 0.603 0.599 0.505 0.514

More info are shown in the paper

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