{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/ett/papers/ran/1","list_of":"/dataset/ett","dataset":"ETT","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,29],"of":29,"counts":{"papers_with_a_benchmark_row":65,"with_a_code_link":59,"where_syntology_ran_a_sample":29,"not_listed_spam_title":0,"listed":65,"listed_where_code_ran":29,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":28,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":28,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/ett/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/prformer-pyramidal-recurrent-transformer-for","slug":"prformer-pyramidal-recurrent-transformer-for","title":"PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting","date":"2024-08-20","arxiv_id":"2408.10483","rows_on_this_dataset":13,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["usualheart/prformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/prformer-pyramidal-recurrent-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2408.10483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10483"}}}},{"paper":"/paper/fredformer-frequency-debiased-transformer-for","slug":"fredformer-frequency-debiased-transformer-for","title":"Fredformer: Frequency Debiased Transformer for Time Series Forecasting","date":"2024-06-13","arxiv_id":"2406.09009","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["chenzrg/fredformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fredformer-frequency-debiased-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2406.09009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09009"}}}},{"paper":"/paper/timecma-towards-llm-empowered-time-series","slug":"timecma-towards-llm-empowered-time-series","title":"TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment","date":"2024-06-03","arxiv_id":"2406.01638","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["chenxiliu-hnu/timecma"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/timecma-towards-llm-empowered-time-series#ran","syntology_url":"https://syntology.ai/paper/2406.01638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01638"}}}},{"paper":"/paper/vcformer-variable-correlation-transformer","slug":"vcformer-variable-correlation-transformer","title":"VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting","date":"2024-05-19","arxiv_id":"2405.11470","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["csyyn/vcformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/vcformer-variable-correlation-transformer#ran","syntology_url":"https://syntology.ai/paper/2405.11470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11470"}}}},{"paper":"/paper/softs-efficient-multivariate-time-series","slug":"softs-efficient-multivariate-time-series","title":"SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion","date":"2024-04-22","arxiv_id":"2404.14197","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":4,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["secilia-cxy/softs"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/softs-efficient-multivariate-time-series#ran","syntology_url":"https://syntology.ai/paper/2404.14197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14197"}}}},{"paper":"/paper/atfnet-adaptive-time-frequency-ensembled","slug":"atfnet-adaptive-time-frequency-ensembled","title":"ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting","date":"2024-04-08","arxiv_id":"2404.05192","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":7,"official":{"repos":["yhyhyhyhyhy/atfnet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/atfnet-adaptive-time-frequency-ensembled#ran","syntology_url":"https://syntology.ai/paper/2404.05192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05192"}}}},{"paper":"/paper/taming-pre-trained-llms-for-generalised-time","slug":"taming-pre-trained-llms-for-generalised-time","title":"CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning","date":"2024-03-12","arxiv_id":"2403.07300","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["hank0626/calf","hank0626/llata"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/taming-pre-trained-llms-for-generalised-time#ran","syntology_url":"https://syntology.ai/paper/2403.07300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07300"}}}},{"paper":"/paper/convtimenet-a-deep-hierarchical-fully","slug":"convtimenet-a-deep-hierarchical-fully","title":"ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis","date":"2024-03-03","arxiv_id":"2403.01493","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":5,"official":{"repos":["mingyue-cheng/convtimenet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/convtimenet-a-deep-hierarchical-fully#ran","syntology_url":"https://syntology.ai/paper/2403.01493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01493"}}}},{"paper":"/paper/units-building-a-unified-time-series-model","slug":"units-building-a-unified-time-series-model","title":"UniTS: A Unified Multi-Task Time Series Model","date":"2024-02-29","arxiv_id":"2403.00131","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":28,"samples_ran":19,"samples_constructed":9,"samples_ran_checked":19,"samples_ran_instrument_failed":0,"samples_unverified":9,"pointer_only_for_licence":0,"official":{"repos":["mims-harvard/UniTS"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["found_in_text","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/units-building-a-unified-time-series-model#ran","syntology_url":"https://syntology.ai/paper/2403.00131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00131"}}}},{"paper":"/paper/generative-pretrained-hierarchical","slug":"generative-pretrained-hierarchical","title":"Generative Pretrained Hierarchical Transformer for Time Series Forecasting","date":"2024-02-26","arxiv_id":"2402.16516","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["icantnamemyself/gpht"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/generative-pretrained-hierarchical#ran","syntology_url":"https://syntology.ai/paper/2402.16516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16516"}}}},{"paper":"/paper/pathformer-multi-scale-transformers-with","slug":"pathformer-multi-scale-transformers-with","title":"Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting","date":"2024-02-04","arxiv_id":"2402.05956","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":5,"pointer_only_for_licence":11,"official":{"repos":["decisionintelligence/pathformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pathformer-multi-scale-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2402.05956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05956"}}}},{"paper":"/paper/rethinking-channel-dependence-for","slug":"rethinking-channel-dependence-for","title":"Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators","date":"2024-01-31","arxiv_id":"2401.17548","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":4,"samples_unverified":3,"pointer_only_for_licence":11,"official":{"repos":["sjtu-dmtai/lift","sjtu-quant/lift"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/rethinking-channel-dependence-for#ran","syntology_url":"https://syntology.ai/paper/2401.17548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17548"}}}},{"paper":"/paper/ttms-fast-multi-level-tiny-time-mixers-for","slug":"ttms-fast-multi-level-tiny-time-mixers-for","title":"Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series","date":"2024-01-08","arxiv_id":"2401.03955","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":4,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["ibm-granite/granite-tsfm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ttms-fast-multi-level-tiny-time-mixers-for#ran","syntology_url":"https://syntology.ai/paper/2401.03955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03955"}}}},{"paper":"/paper/basisformer-attention-based-time-series-1","slug":"basisformer-attention-based-time-series-1","title":"BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis","date":"2023-10-31","arxiv_id":"2310.20496","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":6,"samples_constructed":4,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":12,"official":{"repos":["nzl5116190/basisformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/basisformer-attention-based-time-series-1#ran","syntology_url":"https://syntology.ai/paper/2310.20496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.20496"}}}},{"paper":"/paper/unitime-a-language-empowered-unified-model","slug":"unitime-a-language-empowered-unified-model","title":"UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting","date":"2023-10-15","arxiv_id":"2310.09751","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":4,"official":{"repos":["liuxu77/unitime"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unitime-a-language-empowered-unified-model#ran","syntology_url":"https://syntology.ai/paper/2310.09751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09751"}}}},{"paper":"/paper/a-decoder-only-foundation-model-for-time","slug":"a-decoder-only-foundation-model-for-time","title":"A decoder-only foundation model for time-series forecasting","date":"2023-10-14","arxiv_id":"2310.10688","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["google-research/timesfm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-decoder-only-foundation-model-for-time#ran","syntology_url":"https://syntology.ai/paper/2310.10688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10688"}}}},{"paper":"/paper/itransformer-inverted-transformers-are","slug":"itransformer-inverted-transformers-are","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","date":"2023-10-10","arxiv_id":"2310.06625","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":4,"official":{"repos":["thuml/iTransformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/itransformer-inverted-transformers-are#ran","syntology_url":"https://syntology.ai/paper/2310.06625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06625"}}}},{"paper":"/paper/tempo-prompt-based-generative-pre-trained","slug":"tempo-prompt-based-generative-pre-trained","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","date":"2023-10-08","arxiv_id":"2310.04948","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":2,"samples_unverified":8,"pointer_only_for_licence":0,"official":{"repos":["dc-research/tempo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/tempo-prompt-based-generative-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2310.04948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04948"}}}},{"paper":"/paper/time-llm-time-series-forecasting-by","slug":"time-llm-time-series-forecasting-by","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","date":"2023-10-03","arxiv_id":"2310.01728","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":7,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/time-llm-time-series-forecasting-by#ran","syntology_url":"https://syntology.ai/paper/2310.01728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01728"}}}},{"paper":"/paper/patchmixer-a-patch-mixing-architecture-for","slug":"patchmixer-a-patch-mixing-architecture-for","title":"PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting","date":"2023-10-01","arxiv_id":"2310.00655","rows_on_this_dataset":4,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["Zeying-Gong/PatchMixer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/patchmixer-a-patch-mixing-architecture-for#ran","syntology_url":"https://syntology.ai/paper/2310.00655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00655"}}}},{"paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","slug":"tsmixer-lightweight-mlp-mixer-model-for","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","date":"2023-06-14","arxiv_id":"2306.09364","rows_on_this_dataset":15,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["ibm/tsfm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/tsmixer-lightweight-mlp-mixer-model-for#ran","syntology_url":"https://syntology.ai/paper/2306.09364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09364"}}}},{"paper":"/paper/learning-structured-components-towards","slug":"learning-structured-components-towards","title":"Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting","date":"2023-05-22","arxiv_id":"2305.13036","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":2,"official":{"repos":["JLDeng/SCNN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-structured-components-towards#ran","syntology_url":"https://syntology.ai/paper/2305.13036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13036"}}}},{"paper":"/paper/long-term-forecasting-with-tide-time-series","slug":"long-term-forecasting-with-tide-time-series","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","date":"2023-04-17","arxiv_id":"2304.08424","rows_on_this_dataset":4,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["WenjieDu/PyPOTS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/long-term-forecasting-with-tide-time-series#ran","syntology_url":"https://syntology.ai/paper/2304.08424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08424"}}}},{"paper":"/paper/a-time-series-is-worth-64-words-long-term","slug":"a-time-series-is-worth-64-words-long-term","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","date":"2022-11-27","arxiv_id":"2211.14730","rows_on_this_dataset":4,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":30,"samples_ran":17,"samples_constructed":2,"samples_ran_checked":16,"samples_ran_instrument_failed":1,"samples_unverified":13,"pointer_only_for_licence":0,"official":{"repos":["yuqinie98/patchtst"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-time-series-is-worth-64-words-long-term#ran","syntology_url":"https://syntology.ai/paper/2211.14730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14730"}}}},{"paper":"/paper/are-transformers-effective-for-time-series","slug":"are-transformers-effective-for-time-series","title":"Are Transformers Effective for Time Series Forecasting?","date":"2022-05-26","arxiv_id":"2205.13504","rows_on_this_dataset":6,"code_links":10,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":19,"samples_ran":13,"samples_constructed":4,"samples_ran_checked":13,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":13,"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/are-transformers-effective-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2205.13504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13504"}}}},{"paper":"/paper/film-frequency-improved-legendre-memory-model","slug":"film-frequency-improved-legendre-memory-model","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","date":"2022-05-18","arxiv_id":"2205.08897","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":17,"samples_constructed":2,"samples_ran_checked":7,"samples_ran_instrument_failed":10,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/film-frequency-improved-legendre-memory-model#ran","syntology_url":"https://syntology.ai/paper/2205.08897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08897"}}}},{"paper":"/paper/autoformer-decomposition-transformers-with","slug":"autoformer-decomposition-transformers-with","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","date":"2021-06-24","arxiv_id":"2106.13008","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":16,"samples_ran":13,"samples_constructed":10,"samples_ran_checked":13,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["thuml/autoformer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":7,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/autoformer-decomposition-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2106.13008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.13008"}}}},{"paper":"/paper/time-series-is-a-special-sequence-forecasting","slug":"time-series-is-a-special-sequence-forecasting","title":"SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction","date":"2021-06-17","arxiv_id":"2106.09305","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/time-series-is-a-special-sequence-forecasting#ran","syntology_url":"https://syntology.ai/paper/2106.09305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09305"}}}},{"paper":"/paper/informer-beyond-efficient-transformer-for","slug":"informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","date":"2020-12-14","arxiv_id":"2012.07436","rows_on_this_dataset":2,"code_links":14,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":76,"samples_ran":62,"samples_constructed":53,"samples_ran_checked":62,"samples_ran_instrument_failed":0,"samples_unverified":14,"pointer_only_for_licence":13,"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed","named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/informer-beyond-efficient-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2012.07436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.07436"}}}}],"record_sha256":"39ef117e2b530b6b7c2fc4d8fa65ea309c177af74d23fb5d907012f37004c11a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}