{"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/rethinking-channel-dependence-for","title":"Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators","arxiv_id":"2401.17548","date":"2024-01-31","proceeding":null,"authors":["Lifan Zhao","Yanyan Shen"],"abstract":"Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods miss potential opportunities in utilizing channel dependence for accurate predictions. We argue that there exist locally stationary lead-lag relationships between variates, i.e., some lagged variates may follow the leading indicators within a short time period. Exploiting such channel dependence is beneficial since leading indicators offer advance information that can be used to reduce the forecasting difficulty of the lagged variates. In this paper, we propose a new method named LIFT that first efficiently estimates leading indicators and their leading steps at each time step and then judiciously allows the lagged variates to utilize the advance information from leading indicators. LIFT plays as a plugin that can be seamlessly collaborated with arbitrary time series forecasting methods. Extensive experiments on six real-world datasets demonstrate that LIFT improves the state-of-the-art methods by 5.5% in average forecasting performance. Our code is available at https://github.com/SJTU-Quant/LIFT.","url_abs":"https://arxiv.org/abs/2401.17548v6","url_pdf":"https://arxiv.org/pdf/2401.17548v6.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":"rethinking-channel-dependence-for","repo_url":"https://github.com/sjtu-dmtai/lift","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"rethinking-channel-dependence-for","repo_url":"https://github.com/sjtu-quant/lift","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-forecasting-on-etth1-336-1","task":"Time Series Forecasting","dataset":"ETTh1 (336) Multivariate","model":"PatchTST + LIFT","rank_in_archive_order":31,"of":72,"metrics":{"MAE":"0.435","MSE":"0.433"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-etth1-336-1","task":"Time Series Forecasting","dataset":"ETTh1 (336) Multivariate","model":"DLinear + LIFT","rank_in_archive_order":39,"of":72,"metrics":{"MAE":"0.453","MSE":"0.453"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.17548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17548"}},"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":"deterministic:regex_extraction","url":"https://github.com/SJTU-DMTai/LIFT","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sjtu-dmtai/lift","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sjtu-quant/lift","reach":null}],"summary":{"ran":5,"ran_honours":1,"ran_draft_wrong":1,"ran_violates":1,"unverified":3},"by_repo_kind":{"official":{"samples":11,"ran":8,"repositories":2}},"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":11,"samples":[{"code_sha256_prefix":"de8100ea138b1a4d","entry":"ComplexLinear","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"de8100ea138b1a4d"}},{"code_sha256_prefix":"16b9b78d429636b7","entry":"PositionalEncoding","repo":"SJTU-DMTai/LIFT","repo_kind":"official","path":"layers/PatchTST_layers.py","file_url":"https://github.com/SJTU-DMTai/LIFT/blob/HEAD/layers/PatchTST_layers.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"16b9b78d429636b7"}},{"code_sha256_prefix":"e7a794c3d5786636","entry":"cross_corr_coef","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e7a794c3d5786636"}},{"code_sha256_prefix":"c8f9f310e7e40636","entry":"estimate_indicator","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c8f9f310e7e40636"}},{"code_sha256_prefix":"72b9272542f279c6","entry":"get_activation_fn","repo":"SJTU-DMTai/LIFT","repo_kind":"official","path":"layers/PatchTST_layers.py","file_url":"https://github.com/SJTU-DMTai/LIFT/blob/HEAD/layers/PatchTST_layers.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"72b9272542f279c6"}},{"code_sha256_prefix":"b89d71d4cfdaa92d","entry":"instance_norm","repo":"sjtu-dmtai/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-dmtai/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b89d71d4cfdaa92d"}},{"code_sha256_prefix":"abd5ad5f9040ea25","entry":"shifted_leader_seq","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"abd5ad5f9040ea25"}},{"code_sha256_prefix":"ece7c69991e1b54f","entry":"str_to_bool","repo":"sjtu-dmtai/lift","repo_kind":"official","path":"run_longExp.py","file_url":"https://github.com/sjtu-dmtai/lift/blob/HEAD/run_longExp.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ece7c69991e1b54f"}},{"code_sha256_prefix":"c3fc6992d1e467f3","entry":"Coord2dPosEncoding","repo":"SJTU-DMTai/LIFT","repo_kind":"official","path":"layers/PatchTST_layers.py","file_url":"https://github.com/SJTU-DMTai/LIFT/blob/HEAD/layers/PatchTST_layers.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c3fc6992d1e467f3"}},{"code_sha256_prefix":"0fe09ccead991c9d","entry":"FilterFactory","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0fe09ccead991c9d"}},{"code_sha256_prefix":"a12e5331bf2308b1","entry":"LeadRefiner","repo":"sjtu-quant/lift","repo_kind":"official","path":"models/LIFT.py","file_url":"https://github.com/sjtu-quant/lift/blob/HEAD/models/LIFT.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a12e5331bf2308b1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}