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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Deep Neural Networks for Sales Forecasting
 Tyrpáková, Natália ; Pilát, Martin (advisor) ; Mrázová, Iveta (referee)
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)
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Finanční plánování v podniku
 Voráček, Lukáš ; Hnilica, Jiří (advisor)
The aim of this study is to certify the legitimacy of employing quantitative methods in the day-to-day business practice. The task is approached as a case study of a real-life financial planning process. I work with the financial data of POS Media Czech Republic (a media company providing point-of-sale advertising solutions). My intention is to simulate the projection of a pro forma income statement with the use of quantitative methods. More specifically, I am applying time series prediction techniques in order to forecast POS Media's sales. The goal is, first, to demonstrate that quantitative techniques can be handled even with limited statistical background and, second, to discuss the relevancy of the obtained results. In the methodical part of my thesis I deal with the theoretical aspects of financial planning. I further describe various methods of sales forecasting (qualitative vs. quantitative). Special emphasis is put on time series prediction methods. In the application part I provide a short description of POS Media and its business. I use time series decomposition techniques to predict POS Media's sales in 2012. Consequently, I outline the rest of the pro forma income statement.
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