{"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/3","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,212],"of":212,"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","prev":"/task/demand-forecasting/papers/2","next":null,"papers":[{"url":null,"slug":"approximate-bayesian-inference-in-linear","title":"Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale","date":"2017-09-22","arxiv_id":"1709.07638","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-hotel-room-demand-forecasting-with","title":"Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning","date":"2017-09-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-regularization-in-marketing-and","title":"Sparse Regularization in Marketing and Economics","date":"2017-09-01","arxiv_id":"1709.00379","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approaches-to-energy","title":"Machine Learning Approaches to Energy Consumption Forecasting in Households","date":"2017-06-29","arxiv_id":"1706.09648","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-forecasting-of-passenger-demand","title":"Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach","date":"2017-06-20","arxiv_id":"1706.06279","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-intermittent-demand-forecasting-for","title":"Bayesian Intermittent Demand Forecasting for Large Inventories","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-price-optimization-via-network","title":"Large-Scale Price Optimization via Network Flow","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inertial-regularization-and-selection-irs","title":"Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity","date":"2016-10-23","arxiv_id":"1610.07216","repositories_listed":0,"syntology":null},{"url":null,"slug":"electricity-demand-forecasting-by-multi-task","title":"Electricity Demand Forecasting by Multi-Task Learning","date":"2015-12-27","arxiv_id":"1512.08178","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-time-series-prediction-with","title":"High-dimensional Time Series Prediction with Missing Values","date":"2015-09-28","arxiv_id":"1509.08333","repositories_listed":0,"syntology":null},{"url":null,"slug":"interval-forecasting-of-electricity-demand-a","title":"Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework","date":"2014-06-15","arxiv_id":"1406.3792","repositories_listed":0,"syntology":null},{"url":null,"slug":"household-electricity-demand-forecasting","title":"Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods","date":"2014-04-01","arxiv_id":"1404.0200","repositories_listed":0,"syntology":null}],"record_sha256":"b6bfb382845129b190b03c3e84c4788238aa9c2de4c348ff55da2f4561d74108","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}