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 APA Style APA-ALL Style AAA 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). Improving short-term ride-hauling demand predictions by utilizing bursty geo-tagged Tweet clusters. In Business Analytics & Management.http://hdl.handle.net/2105/57512

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