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Every additional variable – unless perfectly related to the outcome metric (which most are not) – will provide less value as the model gets smarter. You need to work out which features provide significant value fast so you can drive down your forecasting error rate. Let me tell you about how intelligent event data does exactly that.","2020-10-08",{"id":359,"documentId":360,"name":361,"alternativeText":361,"caption":16,"width":120,"height":121,"formats":362,"hash":393,"ext":21,"mime":24,"size":394,"url":395,"previewUrl":16,"provider":41,"provider_metadata":16,"createdAt":396,"updatedAt":396,"publishedAt":397,"focalPoint":16},457,"cnic5mv9qj0zelpwgta8xrc9","Demand forecasting models - how to reduce MAPE\u002Ferror rate",{"large":363,"small":369,"medium":375,"xsmall":381,"thumbnail":387},{"ext":21,"url":364,"hash":365,"mime":24,"name":366,"path":16,"size":367,"width":128,"height":129,"sizeInBytes":368},"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Flarge_error_rate_6fa5651a56.png","large_error_rate_6fa5651a56","large_Demand forecasting models - how to reduce MAPE\u002Ferror rate",234.04,234035,{"ext":21,"url":370,"hash":371,"mime":24,"name":372,"path":16,"size":373,"width":75,"height":136,"sizeInBytes":374},"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Fsmall_error_rate_6fa5651a56.png","small_error_rate_6fa5651a56","small_Demand forecasting models - how to reduce MAPE\u002Ferror rate",89.21,89214,{"ext":21,"url":376,"hash":377,"mime":24,"name":378,"path":16,"size":379,"width":83,"height":143,"sizeInBytes":380},"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Fmedium_error_rate_6fa5651a56.png","medium_error_rate_6fa5651a56","medium_Demand forecasting models - how to reduce MAPE\u002Ferror rate",191.43,191427,{"ext":21,"url":382,"hash":383,"mime":24,"name":384,"path":16,"size":385,"width":27,"height":150,"sizeInBytes":386},"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Fxsmall_error_rate_6fa5651a56.png","xsmall_error_rate_6fa5651a56","xsmall_Demand forecasting models - how to reduce MAPE\u002Ferror rate",9.2,9200,{"ext":21,"url":388,"hash":389,"mime":24,"name":390,"path":16,"size":391,"width":35,"height":36,"sizeInBytes":392},"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Fthumbnail_error_rate_6fa5651a56.png","thumbnail_error_rate_6fa5651a56","thumbnail_Demand forecasting models - how to reduce MAPE\u002Ferror rate",16.64,16639,"error_rate_6fa5651a56",85.34,"https:\u002F\u002Fbright-comfort-e10654a499.media.strapiapp.com\u002Ferror_rate_6fa5651a56.png","2026-01-08T01:27:25.488Z","2026-01-08T01:27:25.489Z",[399],{"id":45,"documentId":46,"topic":47,"slug":48,"title":49,"description":50,"contentfulId":51,"createdAt":52,"updatedAt":52,"publishedAt":53},{"id":36,"documentId":401,"title":402,"slug":403,"intro":404,"publishDate":405,"image":406,"topics":436},"nzlnr2iz98u9ub6hi74yu6gl","Every US college’s key dates and COVID restrictions verified and in one place: PredictHQ’s Academic Events category","academic-events","Every US college has different key dates, student populations and COVID-19 responses. 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Our data source is not just made up of real-time and scheduled events from around the world, it also contains additional location and event related data that allows us to connect and rank our data. This in turn provides a far more contextual experience than just viewing basic event information. With this in mind we have created a category to help give you the latest on all things relating to event data, mash-ups, connections, new data sets, algorithms and much more.  ","2pFAupY0OEXOeQXLiNxcvy","2026-01-09T01:32:02.841Z","2026-01-09T01:32:02.860Z",355,{"items":449,"total":284},[450],{"id":451,"documentId":452,"title":453,"slug":454,"intro":455,"publishDate":456,"image":457,"topics":497,"author":506},1054,"voc0aci4wtmm3m05suefluiq","Why Demand Forecasts Break without Real-World Context","why-demand-forecasts-break","We analyzed $7.2 trillion of global demand data and found that 60% of demand variability is caused by real-world context. Now if you aren’t a data scientist, variability is just the fluctuation in demand. And real-world context is a phrase that represents the aggregate of the events happening around each of your locations. 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You know the Super Bowl is coming, and you know the local college football team plays on Saturdays. 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