{"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":"/task/demand-forecasting/papers/ran/1","list_of":"/task/demand-forecasting","task":"Demand Forecasting","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_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'","n_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)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","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 task or check it against the task'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.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,6],"of":6,"counts":{"archive_papers_tagged":212,"with_a_code_link":34,"where_syntology_ran_a_sample":6,"not_listed_spam_title":0,"listed":212,"listed_where_code_ran":6,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":6,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":6,"listed_every_run_a_failure_of_syntologys_instrument":0,"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 the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/demand-forecasting/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/job-sdf-a-multi-granularity-dataset-for-job","slug":"job-sdf-a-multi-granularity-dataset-for-job","title":"Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking","date":"2024-06-17","arxiv_id":"2406.11920","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/job-sdf-a-multi-granularity-dataset-for-job#ran","syntology_url":"https://syntology.ai/paper/2406.11920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11920"}},"official":{"repos":["job-sdf/benchmark"],"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"]}}},{"url":"/paper/scalable-spatiotemporal-prediction-with","slug":"scalable-spatiotemporal-prediction-with","title":"Scalable Spatiotemporal Prediction with Bayesian Neural Fields","date":"2024-03-12","arxiv_id":"2403.07657","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scalable-spatiotemporal-prediction-with#ran","syntology_url":"https://syntology.ai/paper/2403.07657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07657"}},"official":{"repos":["google/bayesnf"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-spatio-temporal-forecasting-of","slug":"deep-spatio-temporal-forecasting-of","title":"Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand","date":"2021-06-21","arxiv_id":"2106.10940","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-spatio-temporal-forecasting-of#ran","syntology_url":"https://syntology.ai/paper/2106.10940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10940"}},"official":{"repos":["fbohu/Deep-Spatio-Temporal-Forecasting-of-Electrical-Vehicle-Charging-Demand"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/intermittent-demand-forecasting-with-deep","slug":"intermittent-demand-forecasting-with-deep","title":"Intermittent Demand Forecasting with Deep Renewal Processes","date":"2019-11-23","arxiv_id":"1911.10416","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/intermittent-demand-forecasting-with-deep#ran","syntology_url":"https://syntology.ai/paper/1911.10416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10416"}},"official":null}},{"url":"/paper/high-dimensional-multivariate-forecasting","slug":"high-dimensional-multivariate-forecasting","title":"High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes","date":"2019-10-07","arxiv_id":"1910.03002","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/high-dimensional-multivariate-forecasting#ran","syntology_url":"https://syntology.ai/paper/1910.03002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03002"}},"official":{"repos":["mbohlkeschneider/gluon-ts"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/think-globally-act-locally-a-deep-neural","slug":"think-globally-act-locally-a-deep-neural","title":"Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting","date":"2019-05-09","arxiv_id":"1905.03806","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/think-globally-act-locally-a-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1905.03806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.03806"}},"official":{"repos":["rajatsen91/deepglo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}}],"record_sha256":"b9606230e4355faa92be8a00b2ae7e09e84bfc3c7d31a3029f66863cae285b84","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}