Predicting the future with the past – demand forecasting for a fast moving commodity trading firm

Additional Metadata
Keywords	Demand forecast, forecast accuracy, commodity trading firm, linear models, ARIMA models, Artifinal Neural Networks, State Space Models
Thesis Advisor	Bicer, Isik, Pennings, C.L.P.
Persistent URL	hdl.handle.net/2105/40914
Series	Supply Chain 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	Zhou, Amy. (2017, August 31). Predicting the future with the past – demand forecasting for a fast moving commodity trading firm. Supply Chain Management. Retrieved from http://hdl.handle.net/2105/40914

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