[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-forecasting-model-adds-an-agent-that-knows-when-to-ignore-hype":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},5177,"forecasting-model-adds-an-agent-that-knows-when-to-ignore-hype","Forecasting Model Adds an Agent That Knows When to Ignore Hype","ReasonCast lets demand-forecasting AI selectively use event context like holidays and promotions, boosting accuracy without letting hype skew stable periods.","A new forecasting framework called ReasonCast teaches an AI agent to decide, case by case, whether a holiday or promotion should actually change a sales prediction.\n\nResearchers built ReasonCast to pair a time-series forecasting model with an agent that reads event context, such as promotions, holidays, price changes, and platform interventions, and decides whether that text should influence the numeric forecast at all. Instead of feeding raw text into the model, it converts event knowledge into structured fields covering relevance, demand direction, timing, and amplitude, which then adjust the forecast through separate additive (trend correction) and multiplicative (level shift) paths. The system trains in three stages: one to learn the structured fields, one to calibrate direction, shape, and amplitude judgments, and one that uses a frozen forecaster to reward only the interventions that actually improve accuracy. In testing, it cut forecast error (WMAPE) by 3.29 percentage points on holiday-sensitive categories, 1.25 points on mega-sale categories, and 0.47 points on M5 event windows.\n\nThis is a story about restraint, not raw power. The paper's most telling number is that applying semantic reasoning to every forecast, including stable, non-event periods, raises error by 1.68 percentage points. That inverts the usual pitch for text-augmented forecasting, which tends to assume more context is always better.\n\nIt's a reminder that in forecasting, as in journalism, knowing when to ignore the news matters as much as reading it.","[\"ai\",\"forecasting\",\"machine-learning\",\"time-series\"]","2026-08-18T04:00:00.000Z","2026-08-18T08:21:00.310Z","2026-08-18T08:21:12.111Z","published",null,[],"ai",[24,26,27,28],"forecasting","machine-learning","time-series",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.15291",0,{"sections":35},[36,40,44,49,54,59,64,69,74,78,83,88,93,98],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",3293,"2026-08-20T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":39},"Security","security",435,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",210,"2026-08-19T09:32:27.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",140,"2026-08-19T18:25:42.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Consumer Tech","consumer-tech",95,"2026-08-18T16:05:00.000Z",{"name":65,"slug":66,"count":67,"latest_published_at":68},"Science","science",90,"2026-08-19T18:41:02.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Software","software",73,"2026-08-18T07:51:50.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":18},"Dev Tools","dev-tools",69,{"name":79,"slug":80,"count":81,"latest_published_at":82},"Startups","startups",47,"2026-08-19T19:13:46.000Z",{"name":84,"slug":85,"count":86,"latest_published_at":87},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":89,"slug":90,"count":91,"latest_published_at":92},"General","general",33,"2026-08-18T22:18:13.000Z",{"name":94,"slug":95,"count":96,"latest_published_at":97},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]