Sales forecasting is an essential part of supply chain management. In retail business, accurate sales forecasts lead to significant cost reductions. Statistical methods that are commonly used for sales forecasting often overlook important aspects unique for the sales time series, which lowers the forecast accuracy. In this thesis we explore whether it is possible to improve short-term sales forecasting by employing deep neural networks. This thesis analyzes performance of various traditional deep neural network designs and proposes a novel architecture. It also explores several data preprocessing methods, both traditional and non-traditional, which turns out to be a crucial part of sales forecasting using deep neural networks. The best methods of deep neural network approach that we found are then compared to other forecasting methods such as traditional neural networks or exponential smoothing. Powered by TCPDF (www.tcpdf.org)
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:352788 |
| Date | January 2016 |
| Creators | Tyrpáková, Natália |
| Contributors | Pilát, Martin, Mrázová, Iveta |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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