Papers › Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time...
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
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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Code
Syntology Ran 5 of 10 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 2 ran · fixture could not drive it; 1 ran with no contract checked.
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Code Syntology ran Syntology
10 samples harvested; 5 ran; 1 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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