Napovedovanje prodaje zdravil z uporabo naprednih metod napovedovanja časovnih vrst : magistrsko delo

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Title:	Napovedovanje prodaje zdravil z uporabo naprednih metod napovedovanja časovnih vrst : magistrsko delo
Authors:	ID Pudič, Žan (Author) ID Ojsteršek, Robert (Mentor) More about this mentor... ID Frešer, Blaž (Comentor)
Files:	MAG_Pudic_Zan_2023.pdf (3,58 MB) MD5: 3CFDC746D171A9BFE0C19445D3D8A8C0
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FS - Faculty of Mechanical Engineering
Abstract:	Magistrsko delo zajema uporabo naprednih modelov za napovedovanje prodaje zdravil. Cilj dela je s pomočjo naprednih metod napovedovanja v programskem okolju R, postaviti napovedovalne modele za posamezne skupine zdravil, ki bodo v izbranih intervalih zaupanja uspešno napovedali prihodnjo prodajo. V delu smo za potrebe napovedovanja uporabili modele kot so ARIMA, CNN, Holt-Winters pri čemer smo te primerjali z naivno metodo napovedovanj in tako ocenili njihovo sposobnost napovedovanja. Prav tako smo v delu podrobno analizirali izhode v fazi kreiranja modelov na podlagi katerih smo izvedli nadaljnjo selekcijo modelov s katerimi lahko uspešno napovemo prihodnjo prodajo. Uspešne napovedi smo izvedli pri vseh skupinah zdravil. V delu je najuspešnejšo napoved pri skupinah zdravil M01AB. M01AE, N02BA, N05C, R03 in R06 imel ARIMA model. Prodajo v skupinah zdravil N05C in N02BE pa lahko napovedujemo zgolj z uporabo CNN, saj noben izmed preostalih modelov ni uspel dovolj dobro zajeti informacij znotraj časovne vrste, da bi lahko z njim podali napoved.
Keywords:	napredne metode napovedovanja, časovne vrste, napovedovanje prodaje, napovedovalni modeli, ARIMA, CNN, Holt-Winters.
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[Ž. Pudič]
Year of publishing:	2023
Number of pages:	1 spletni vir (1 datoteka PDF (XV, 74 f))
PID:	20.500.12556/DKUM-85212
UDC:	[005.521:658.8]:519.246(043.2)
COBISS.SI-ID:	169616643
Publication date in DKUM:	26.09.2023
Views:	607
Downloads:	135
Metadata:
Categories:	KTFMB - FS
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	22.08.2023

Secondary language

Language:	English
Title:	Forecasting sales of medicines using advanced time series forecasting methods
Abstract:	The master thesis covers the use of advanced models for predicting sales of medicines. The aim of the work is to use advanced forecasting methods in the R software environment to build predictive models for specific groups of medicines that will successfully predict future sales within selected confidence intervals. In this work, we have used models such as ARIMA, CNN, Holt-Winters and compared them with naive forecasting method to assess their predictive ability. We have also analysed in detail the outputs of the model in creation phase to further select the models that can successfully predict future sales. Successful predictions were made for all groups of medicines. The most successful prediction in the work for drug groups. M01AE, N02BA, N05C, R03 and R06 had ARIMA model. However, the drug groups N05C and N02BE sales could only be predicted using CNN, as none of the remaining models were able to capture the information within the time series well enough to provide a prediction.
Keywords:	advanced forecasting methods, time series, sales forecasting, forecasting models, ARIMA, CNN, Holt-Winters.

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