Improving short-term ride-hauling demand predictions by utilizing bursty geo-tagged Tweet clusters

Additional Metadata
Keywords	lstm, time-series, social media, prediction, spatiotemporal
Thesis Advisor	Wan, Charles, Szymanowski, Maciej
Persistent URL	hdl.handle.net/2105/57512
Series	Business Analytics & Management
Organisation	Rotterdam School of Management
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	M. Roquas (Milo). (2021, July 15). Improving short-term ride-hauling demand predictions by utilizing bursty geo-tagged Tweet clusters. Business Analytics & Management. Retrieved from http://hdl.handle.net/2105/57512

Publication