[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-forecasting-model-predicts-whether-data-will-even-show-up":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},9321,"new-forecasting-model-predicts-whether-data-will-even-show-up","New Forecasting Model Predicts Whether Data Will Even Show Up","Timeflies treats missing sensor data as part of the forecast, not a gap to patch, and claims it beats rivals on messy real-world series.","Researchers built a forecasting model that first asks whether a sensor will report anything at all, then predicts what it will say.\n\nA team describes Timeflies, a time-series forecasting framework that treats whether an observation will exist as a prediction problem in its own right, not an afterthought. Most forecasting tools, including modern continuous-time approaches like Neural ODEs, quietly assume the timestamps of future valid readings are already known, an assumption that falls apart with sensors that go dormant, data that arrives late, or systems that only log on an event. Timeflies instead runs two linked streams, one tracking whether an observation will occur and one tracking its value, tied together through modules for reliability-aware embedding and observation-guided dependency modeling. The team also built a benchmark called Shadow, mixing public datasets with real industrial data, and introduced a new metric, Observation-Value Joint Entropy, to score how well a model handles both problems at once.\n\nMost forecasting research optimizes for accuracy on data that is already there, which works fine in a lab and badly in a factory, a hospital monitor, or any sensor network with real-world gaps. By scoring whether a model can anticipate its own blind spots, Timeflies targets a failure mode that is invisible in benchmarks built from clean, resampled data but costly in production, where a missed reading can look identical to an uneventful one.\n\nThe code and benchmark are open on GitHub, so the real test isn't the paper's numbers, it's whether anyone outside the original lab bothers to reproduce them.","[\"ai\",\"time-series-forecasting\",\"machine-learning\",\"research\"]","2026-10-01T04:00:00.000Z","2026-10-02T09:18:13.886Z","2026-10-02T09:18:15.481Z","published",null,[],"ai",[24,26,27,28],"time-series-forecasting","machine-learning","research",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.13571",0,{"sections":35},[36,39,43,47,52,57,61,66,71,75,80,85,90,95],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",5690,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",820,{"name":44,"slug":45,"count":46,"latest_published_at":18},"Policy","policy",430,{"name":48,"slug":49,"count":50,"latest_published_at":51},"Deals","deals",308,"2026-10-01T12:30:00.000Z",{"name":53,"slug":54,"count":55,"latest_published_at":56},"Hardware","hardware",196,"2026-09-30T13:00:00.000Z",{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",165,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",150,"2026-10-01T11:59:27.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Dev Tools","dev-tools",93,"2026-10-01T02:30:48.000Z",{"name":72,"slug":73,"count":69,"latest_published_at":74},"Software","software","2026-09-30T21:41:11.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Startups","startups",84,"2026-09-30T20:39:09.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",51,"2026-09-30T16:24:30.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",7,"2026-10-01T09:00:00.000Z"]