{"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/timemachine-a-time-series-is-worth-4-mambas","title":"TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting","arxiv_id":"2403.09898","date":"2024-03-14","proceeding":null,"authors":["Md Atik Ahamed","Qiang Cheng"],"abstract":"Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining linear scalability and small memory footprints. TimeMachine exploits the unique properties of time series data to produce salient contextual cues at multi-scales and leverage an innovative integrated quadruple-Mamba architecture to unify the handling of channel-mixing and channel-independence situations, thus enabling effective selection of contents for prediction against global and local contexts at different scales. Experimentally, TimeMachine achieves superior performance in prediction accuracy, scalability, and memory efficiency, as extensively validated using benchmark datasets. Code availability: https://github.com/Atik-Ahamed/TimeMachine","url_abs":"https://arxiv.org/abs/2403.09898v2","url_pdf":"https://arxiv.org/pdf/2403.09898v2.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":"timemachine-a-time-series-is-worth-4-mambas","repo_url":"https://github.com/atik-ahamed/timemachine","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"mamba","task_name":"Mamba"},{"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":"TimeMachine","rank_in_archive_order":28,"of":72,"metrics":{"MAE":"0.421","MSE":"0.429"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2403.09898","atlas_url":"https://app.syntology.ai/?focus=2403.09898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09898"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/atik-ahamed/timemachine","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"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":"8c76ef42a9443f81","entry":"CORR","repo":"atik-ahamed/timemachine","repo_kind":"official","path":"TimeMachine_supervised/utils/metrics.py","file_url":"https://github.com/atik-ahamed/timemachine/blob/HEAD/TimeMachine_supervised/utils/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8c76ef42a9443f81"}},{"code_sha256_prefix":"75f44993b096bf76","entry":"MAE","repo":"atik-ahamed/timemachine","repo_kind":"official","path":"TimeMachine_supervised/utils/metrics.py","file_url":"https://github.com/atik-ahamed/timemachine/blob/HEAD/TimeMachine_supervised/utils/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"75f44993b096bf76"}},{"code_sha256_prefix":"b40a11875ebd0cd2","entry":"RSE","repo":"atik-ahamed/timemachine","repo_kind":"official","path":"TimeMachine_supervised/utils/metrics.py","file_url":"https://github.com/atik-ahamed/timemachine/blob/HEAD/TimeMachine_supervised/utils/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b40a11875ebd0cd2"}},{"code_sha256_prefix":"9a5fcd4ebfc55d03","entry":"time_features","repo":"atik-ahamed/timemachine","repo_kind":"official","path":"TimeMachine_supervised/utils/timefeatures.py","file_url":"https://github.com/atik-ahamed/timemachine/blob/HEAD/TimeMachine_supervised/utils/timefeatures.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9a5fcd4ebfc55d03"}},{"code_sha256_prefix":"f8544563682146e5","entry":"time_features_from_frequency_str","repo":"atik-ahamed/timemachine","repo_kind":"official","path":"TimeMachine_supervised/utils/timefeatures.py","file_url":"https://github.com/atik-ahamed/timemachine/blob/HEAD/TimeMachine_supervised/utils/timefeatures.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f8544563682146e5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}