{"id":8755,"date":"2017-07-06T11:11:14","date_gmt":"2017-07-06T15:11:14","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=8755"},"modified":"2017-07-06T11:13:58","modified_gmt":"2017-07-06T15:13:58","slug":"forecasting-uber-recurrent-neural-networks","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/forecasting-uber-recurrent-neural-networks\/8755\/","title":{"rendered":"Forecasting At Uber With Recurrent Neural Networks"},"content":{"rendered":"Originally published in Uber Engineering, June 9, 2017 At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time. Extreme events\u2014peak travel times such as holidays, concerts,\u00a0inclement\u00a0weather, and sporting events\u2014only heighten the importance of forecasting for operations planning. Calculating\u00a0demand time series forecasting\u00a0during extreme events is a critical component of\u00a0anomaly detection, optimal resource allocation, and budgeting. Although extreme event forecasting is a crucial piece of Uber operations, data sparsity makes accurate prediction challenging. Consider New <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/forecasting-uber-recurrent-neural-networks\/8755\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Originally published in Uber Engineering, June 9, 2017 At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time. Extreme events\u2014peak travel times such as holidays, concerts,\u00a0inclement\u00a0weather, and sporting events\u2014only heighten [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11],"tags":[939,856,954,953],"class_list":["post-8755","post","type-post","status-publish","format-standard","hentry","category-industry-news","tag-forecasting","tag-predictive-forecasting","tag-uber","tag-uber-engineering"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/8755","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=8755"}],"version-history":[{"count":2,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/8755\/revisions"}],"predecessor-version":[{"id":8757,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/8755\/revisions\/8757"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=8755"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=8755"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=8755"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}