{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-structured-components-towards","title":"Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting","arxiv_id":"2305.13036","date":"2023-05-22","proceeding":null,"authors":["Jinliang Deng","Xiusi Chen","Renhe Jiang","Du Yin","Yi Yang","Xuan Song","Ivor W. Tsang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2305.13036v3","url_pdf":"https://arxiv.org/pdf/2305.13036v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-structured-components-towards","repo_url":"https://github.com/JLDeng/SCNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[{"method_slug":"mts","method_name":"MTS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-forecasting-on-etth1-192-1","task":"Time Series Forecasting","dataset":"ETTh1 (192) Multivariate","model":"SCNN","rank_in_archive_order":3,"of":17,"metrics":{"MAE":"0.398","MSE":"0.379"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-ettm1-192-1","task":"Time Series Forecasting","dataset":"ETTm1 (192) Multivariate","model":"SCNN","rank_in_archive_order":4,"of":9,"metrics":{"MSE":"0.327"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-ettm1-96-1","task":"Time Series Forecasting","dataset":"ETTm1 (96) Multivariate","model":"SCNN","rank_in_archive_order":5,"of":9,"metrics":{"MSE":"0.287"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-192-1","task":"Time Series Forecasting","dataset":"ETTm2 (192) Multivariate","model":"SCNN","rank_in_archive_order":6,"of":9,"metrics":{"MSE":"0.221"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-ettm2-96-1","task":"Time Series Forecasting","dataset":"ETTm2 (96) Multivariate","model":"SCNN","rank_in_archive_order":5,"of":9,"metrics":{"MSE":"0.163"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-weather-192","task":"Time Series Forecasting","dataset":"Weather (192)","model":"SCNN","rank_in_archive_order":3,"of":13,"metrics":{"MSE":"0.188"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-weather-96","task":"Time Series Forecasting","dataset":"Weather (96)","model":"SCNN","rank_in_archive_order":2,"of":12,"metrics":{"MSE":"0.142"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.13036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13036"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/JLDeng/SCNN","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"ran_honours":1,"ran_draft_wrong":1,"ran_fixture":2,"unverified":5},"by_repo_kind":{"official":{"samples":10,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2af0df7394e58e06","entry":"conv1d_fft","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/ETSformer_EncDec.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/ETSformer_EncDec.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2af0df7394e58e06"}},{"code_sha256_prefix":"592ea8b254b006db","entry":"get_frequency_modes","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/FourierCorrelation.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/FourierCorrelation.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"592ea8b254b006db"}},{"code_sha256_prefix":"f32036738135c8a8","entry":"get_phi_psi","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/MultiWaveletCorrelation.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/MultiWaveletCorrelation.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f32036738135c8a8"}},{"code_sha256_prefix":"4ef26472da51e3aa","entry":"legendreDer","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/MultiWaveletCorrelation.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/MultiWaveletCorrelation.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4ef26472da51e3aa"}},{"code_sha256_prefix":"a54c8c5c47c6a8b8","entry":"phi_","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/MultiWaveletCorrelation.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/MultiWaveletCorrelation.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a54c8c5c47c6a8b8"}},{"code_sha256_prefix":"c6c4396a7f11dc3e","entry":"PeriodNorm","repo":"JLDeng/SCNN","repo_kind":"official","path":"models/SCNN.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/models/SCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c6c4396a7f11dc3e"}},{"code_sha256_prefix":"76c65930af87d4c8","entry":"SeasonalExtrapolate","repo":"JLDeng/SCNN","repo_kind":"official","path":"models/SCNN.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/models/SCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"76c65930af87d4c8"}},{"code_sha256_prefix":"aa6cfd64a5b30d55","entry":"SeasonalNorm","repo":"JLDeng/SCNN","repo_kind":"official","path":"models/SCNN.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/models/SCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"aa6cfd64a5b30d55"}},{"code_sha256_prefix":"fd61ceb881a058a2","entry":"get_mask","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/Pyraformer_EncDec.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/Pyraformer_EncDec.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fd61ceb881a058a2"}},{"code_sha256_prefix":"05bdfb126191dbb0","entry":"refer_points","repo":"JLDeng/SCNN","repo_kind":"official","path":"layers/Pyraformer_EncDec.py","file_url":"https://github.com/JLDeng/SCNN/blob/HEAD/layers/Pyraformer_EncDec.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"05bdfb126191dbb0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}