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Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

22 May 2023arXiv:2305.13036archive 2025-07-28

Jinliang Deng, Xiusi Chen, Renhe Jiang, Du Yin, Yi Yang, Xuan Song, Ivor W. Tsang

Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns. In this paper, we develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. We name this framework SCNN, as an acronym of Structured Component-based Neural Network. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. In line with its reverse process, SCNN decouples MTS data into structured and heterogeneous components and then respectively extrapolates the evolution of these components, the dynamics of which are more traceable and predictable than the original MTS. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets. Additionally, we examine SCNN with different configurations and perform in-depth analyses of the properties of SCNN.

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conv1d_fft JLDeng/SCNN/layers/ETSformer_EncDec.py official repository ran fingerprinted MIT (permissive) · 2af0df7394e58e06 · report
get_frequency_modes JLDeng/SCNN/layers/FourierCorrelation.py official repository ran · honoured contract fingerprinted MIT (permissive) · 592ea8b254b006db · report
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SeasonalExtrapolate JLDeng/SCNN/models/SCNN.py official repository unverified MIT (permissive) · 76c65930af87d4c8 · report
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refer_points JLDeng/SCNN/layers/Pyraformer_EncDec.py official repository unverified MIT (permissive) · 05bdfb126191dbb0 · report

Tasks

Multivariate Time Series ForecastingTime SeriesTime Series Forecasting

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Time Series Forecasting ETTh1 (192) Multivariate SCNN MAE 0.398 #3 of 17 Archive leaderboard report
Time Series Forecasting ETTh1 (192) Multivariate SCNN MSE 0.379 #3 of 17 Archive leaderboard report
Time Series Forecasting ETTm1 (192) Multivariate SCNN MSE 0.327 #4 of 9 Archive leaderboard report
Time Series Forecasting ETTm1 (96) Multivariate SCNN MSE 0.287 #5 of 9 Archive leaderboard report
Time Series Forecasting ETTm2 (192) Multivariate SCNN MSE 0.221 #6 of 9 Archive leaderboard report
Time Series Forecasting ETTm2 (96) Multivariate SCNN MSE 0.163 #5 of 9 Archive leaderboard report
Time Series Forecasting Weather (192) SCNN MSE 0.188 #3 of 13 Archive leaderboard report
Time Series Forecasting Weather (96) SCNN MSE 0.142 #2 of 12 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

MTS

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