Napovedovanje maloprodajnih cen mesa na podlagi časovnih vrst : na študijskem programu 2. stopnje Matematika

Kuhar, Eva

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Title:	Napovedovanje maloprodajnih cen mesa na podlagi časovnih vrst : na študijskem programu 2. stopnje Matematika
Authors:	ID Kuhar, Eva (Author) ID Benkovič, Dominik (Mentor) More about this mentor...
Files:	MAG_Kuhar_Eva_2024.pdf (2,79 MB) MD5: E3797D91D9E44798C2E9553CDC16AB48
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FNM - Faculty of Natural Sciences and Mathematics
Abstract:	V magistrskem delu se srečujemo z izzivom napovedovanja cen. Sprašujemo se, kateri so tisti parametri, ki vplivajo na končno ceno izdelka. Obravnavamo dve vrsti modelov napovedovanja cen - model multiple regresije, ki napoveduje ceno s pomočjo drugih parametrov, in sezonski ARIMA model, ki napoveduje cene na podlagi preteklega vzorca podatkov. Podatki, s katerimi gradimo modele, so v obliki časovnih vrst. V prvem delu uredimo podatke v skupno Excel tabelo ter izračunamo vse pomembne statistike. Sledi gradnja modela multiple regresije za posamezne izdelke, nato še gradnja sezonskega ARIMA modela. Modele multiple regresije gradimo v programu EViews, ARIMA modele pa v programu Python. Skozi analizo ugotovimo, da imamo v podatkih prisotno močno sezonsko komponento, zato podatke desezoniramo in zgradimo še model multiple regresije na desezoniranih podatkih. Končna ugotovitev kaže na to, da regresijski model na desezoniranih podatkih najbolje pojasnjuje variabilnost odvisne spremenljivke.
Keywords:	Časovne vrste, model multiple regresije, ARIMA model, sezonskost.
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	E. Kuhar
Year of publishing:	2024
Number of pages:	VIII, 93 f.
PID:	20.500.12556/DKUM-87212
UDC:	519.237(043.2)
COBISS.SI-ID:	190132483
Publication date in DKUM:	26.03.2024
Views:	419
Downloads:	76
Metadata:
Categories:	FNM
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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:	06.03.2024

Secondary language

Language:	English
Title:	Predicting retail meat prices based on time series : magistrsko delo
Abstract:	In the master's thesis, we confront the challenge of price prediction. We inquire about the parameters that influence the final product price. We address two types of price prediction models - a multiple regression model that predicts the price using other parameters, and a seasonal ARIMA model that forecasts prices based on past data patterns. The data used to build the models are in the form of time series. In the first part, we organize the data into a comprehensive Excel table and calculate all relevant statistics. This is followed by the construction of a multiple regression model for individual products, and then the construction of a seasonal ARIMA model. We construct multiple regression models in the EViews software, while ARIMA models are built in Python. Through analysis, we discover a strong seasonal component in the data, so we deseasonalize the data and build a multiple regression model on deseasonalized data. The final conclusion indicates that the variability of the dependent variable is best explained by a regression model on deseasonalized data.
Keywords:	Time series, multiple regression model, ARIMA model, seasonality.

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