TY  -  
T1  - Comparison of forecasting methods for retail data 
SN  -  /  
UR  - URN:NBN:fi:hulib-202006172997; http://hdl.handle.net/10138/316577 
T3  -  
A1  - Länsman, Olá-Mihkku 
A2  -  
PB  - Helsingin yliopisto 
Y1  - 2020 
LA  - eng 
AB  - Demand forecasts are required for optimizing multiple challenges in the retail industry, and they
can be used to reduce spoilage and excess inventory sizes. The classical forecasting methods
provide point forecasts and do not quantify the uncertainty of the process. We evaluate multiple
predictive posterior approximation methods with a Bayesian generalized linear model that
captures weekly and yearly seasonality, changing trends and promotional effects. The model
uses negative binomial as t... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - retail; probabilistic forecasting; Bayesian inference; posterior approximation; none; Algoritmit; Algorithms; Algoritmer; Tietojenkäsittelytieteen maisteriohjelma; Master's Programme in Computer Science; Magisterprogrammet i datavetenskap 
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ER  -