TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Time Series Forecasting	ETTh1 (168) Multivariate	SCINet	MSE	0.408	# 1
Time Series Forecasting	ETTh1 (168) Multivariate	SCINet	MAE	0.417	# 4
Time Series Forecasting	ETTh1 (168) Univariate	SCINet	MSE	0.071	# 1
Time Series Forecasting	ETTh1 (168) Univariate	SCINet	MAE	0.21	# 4
Time Series Forecasting	ETTh1 (24) Multivariate	SCINet	MSE	0.3	# 1
Time Series Forecasting	ETTh1 (24) Multivariate	SCINet	MAE	0.342	# 4
Time Series Forecasting	ETTh1 (24) Univariate	SCINet	MSE	0.029	# 1
Time Series Forecasting	ETTh1 (24) Univariate	SCINet	MAE	0.127	# 5
Time Series Forecasting	ETTh1 (336) Multivariate	SCINet	MSE	0.504	# 10
Time Series Forecasting	ETTh1 (336) Multivariate	SCINet	MAE	0.495	# 4
Time Series Forecasting	ETTh1 (336) Univariate	SCINet	MSE	0.084	# 6
Time Series Forecasting	ETTh1 (336) Univariate	SCINet	MAE	0.234	# 5
Time Series Forecasting	ETTh1 (48) Multivariate	SCINet	MSE	0.361	# 1
Time Series Forecasting	ETTh1 (48) Multivariate	SCINet	MAE	0.388	# 4
Time Series Forecasting	ETTh1 (48) Univariate	SCINet	MSE	0.041	# 1
Time Series Forecasting	ETTh1 (48) Univariate	SCINet	MAE	0.154	# 4
Time Series Forecasting	ETTh1 (720) Multivariate	SCINet	MSE	0.544	# 10
Time Series Forecasting	ETTh1 (720) Multivariate	SCINet	MAE	0.527	# 4
Time Series Forecasting	ETTh1 (720) Univariate	SCINet	MSE	0.099	# 7
Time Series Forecasting	ETTh1 (720) Univariate	SCINet	MAE	0.25	# 6
Time Series Forecasting	ETTh2 (168) Multivariate	SCINet	MSE	0.342	# 1
Time Series Forecasting	ETTh2 (168) Multivariate	SCINet	MAE	0.38	# 4
Time Series Forecasting	ETTh2 (168) Univariate	SCINet	MSE	0.158	# 2
Time Series Forecasting	ETTh2 (168) Univariate	SCINet	MAE	0.311	# 3
Time Series Forecasting	ETTh2 (24) Multivariate	SCINet	MSE	0.18	# 1
Time Series Forecasting	ETTh2 (24) Multivariate	SCINet	MAE	0.263	# 4
Time Series Forecasting	ETTh2 (24) Univariate	SCINet	MSE	0.065	# 1
Time Series Forecasting	ETTh2 (24) Univariate	SCINet	MAE	0.183	# 5
Time Series Forecasting	ETTh2 (336) Multivariate	SCINet	MSE	0.365	# 6
Time Series Forecasting	ETTh2 (336) Multivariate	SCINet	MAE	0.409	# 6
Time Series Forecasting	ETTh2 (336) Univariate	SCINet	MSE	0.166	# 1
Time Series Forecasting	ETTh2 (336) Univariate	SCINet	MAE	0.329	# 9
Time Series Forecasting	ETTh2 (48) Multivariate	SCINet	MSE	0.23	# 1
Time Series Forecasting	ETTh2 (48) Multivariate	SCINet	MAE	0.303	# 4
Time Series Forecasting	ETTh2 (48) Univariate	SCINet	MSE	0.093	# 1
Time Series Forecasting	ETTh2 (48) Univariate	SCINet	MAE	0.227	# 4
Time Series Forecasting	ETTh2 (720) Multivariate	SCINet	MSE	0.475	# 8
Time Series Forecasting	ETTh2 (720) Multivariate	SCINet	MAE	0.488	# 5
Time Series Forecasting	ETTh2 (720) Univariate	SCINet	MSE	0.286	# 11
Time Series Forecasting	ETTh2 (720) Univariate	SCINet	MAE	0.429	# 10

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SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction

17 Jun 2021 · Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, Qiang Xu ·

One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Specifically, SCINet is a recursive downsample-convolve-interact architecture. In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features. By combining these rich features aggregated from multiple resolutions, SCINet effectively models time series with complex temporal dynamics. Experimental results show that SCINet achieves significant forecasting accuracy improvements over both existing convolutional models and Transformer-based solutions across various real-world time series forecasting datasets. Our codes and data are available at https://github.com/cure-lab/SCINet.

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Time Series

Time Series Analysis

Time Series Forecasting

Traffic Prediction

Univariate Time Series Forecasting

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Ranked #1 on Time Series Forecasting on ETTh1 (24) Multivariate (using extra training data)

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Time Series Forecasting	ETTh1 (168) Multivariate	SCINet	MSE	0.408	# 1	Compare
Time Series Forecasting	ETTh1 (168) Multivariate	SCINet	MAE	0.417	# 4	Compare
Time Series Forecasting	ETTh1 (168) Univariate	SCINet	MSE	0.071	# 1	Compare
Time Series Forecasting	ETTh1 (168) Univariate	SCINet	MAE	0.21	# 4	Compare
Time Series Forecasting	ETTh1 (24) Multivariate	SCINet	MSE	0.3	# 1	Compare
Time Series Forecasting	ETTh1 (24) Multivariate	SCINet	MAE	0.342	# 4	Compare
Time Series Forecasting	ETTh1 (24) Univariate	SCINet	MSE	0.029	# 1	Compare
Time Series Forecasting	ETTh1 (24) Univariate	SCINet	MAE	0.127	# 5	Compare
Time Series Forecasting	ETTh1 (336) Multivariate	SCINet	MSE	0.504	# 10	Compare
Time Series Forecasting	ETTh1 (336) Multivariate	SCINet	MAE	0.495	# 4	Compare
Time Series Forecasting	ETTh1 (336) Univariate	SCINet	MSE	0.084	# 6	Compare
Time Series Forecasting	ETTh1 (336) Univariate	SCINet	MAE	0.234	# 5	Compare
Time Series Forecasting	ETTh1 (48) Multivariate	SCINet	MSE	0.361	# 1	Compare
Time Series Forecasting	ETTh1 (48) Multivariate	SCINet	MAE	0.388	# 4	Compare
Time Series Forecasting	ETTh1 (48) Univariate	SCINet	MSE	0.041	# 1	Compare
Time Series Forecasting	ETTh1 (48) Univariate	SCINet	MAE	0.154	# 4	Compare
Time Series Forecasting	ETTh1 (720) Multivariate	SCINet	MSE	0.544	# 10	Compare
Time Series Forecasting	ETTh1 (720) Multivariate	SCINet	MAE	0.527	# 4	Compare
Time Series Forecasting	ETTh1 (720) Univariate	SCINet	MSE	0.099	# 7	Compare
Time Series Forecasting	ETTh1 (720) Univariate	SCINet	MAE	0.25	# 6	Compare
Time Series Forecasting	ETTh2 (168) Multivariate	SCINet	MSE	0.342	# 1	Compare
Time Series Forecasting	ETTh2 (168) Multivariate	SCINet	MAE	0.38	# 4	Compare
Time Series Forecasting	ETTh2 (168) Univariate	SCINet	MSE	0.158	# 2	Compare
Time Series Forecasting	ETTh2 (168) Univariate	SCINet	MAE	0.311	# 3	Compare
Time Series Forecasting	ETTh2 (24) Multivariate	SCINet	MSE	0.18	# 1	Compare
Time Series Forecasting	ETTh2 (24) Multivariate	SCINet	MAE	0.263	# 4	Compare
Time Series Forecasting	ETTh2 (24) Univariate	SCINet	MSE	0.065	# 1	Compare
Time Series Forecasting	ETTh2 (24) Univariate	SCINet	MAE	0.183	# 5	Compare
Time Series Forecasting	ETTh2 (336) Multivariate	SCINet	MSE	0.365	# 6	Compare
Time Series Forecasting	ETTh2 (336) Multivariate	SCINet	MAE	0.409	# 6	Compare
Time Series Forecasting	ETTh2 (336) Univariate	SCINet	MSE	0.166	# 1	Compare
Time Series Forecasting	ETTh2 (336) Univariate	SCINet	MAE	0.329	# 9	Compare
Time Series Forecasting	ETTh2 (48) Multivariate	SCINet	MSE	0.23	# 1	Compare
Time Series Forecasting	ETTh2 (48) Multivariate	SCINet	MAE	0.303	# 4	Compare
Time Series Forecasting	ETTh2 (48) Univariate	SCINet	MSE	0.093	# 1	Compare
Time Series Forecasting	ETTh2 (48) Univariate	SCINet	MAE	0.227	# 4	Compare
Time Series Forecasting	ETTh2 (720) Multivariate	SCINet	MSE	0.475	# 8	Compare
Time Series Forecasting	ETTh2 (720) Multivariate	SCINet	MAE	0.488	# 5	Compare
Time Series Forecasting	ETTh2 (720) Univariate	SCINet	MSE	0.286	# 11	Compare
Time Series Forecasting	ETTh2 (720) Univariate	SCINet	MAE	0.429	# 10	Compare

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Absolute Position Encodings • Adam • BPE • Causal Convolution • Convolution • Dense Connections • Dilated Causal Convolution • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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