Analýza časových řad
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Vysoká škola báňská – Technická univerzita Ostrava
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Abstract
The aim of this master’s thesis was to describe and test several methods for time series analysis.
Thesis will work with a dataset representing hourly natural gas consumption from 2013 to 2019.
A significant part of the analysis will be based on time series decomposition, which will be used
for describing the properties of the series and anomaly detection. Both selected and implemented
methods for prediction fall into the category of machine learning. The programming language used
for the implementation was Python.
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time series, analysis, decomposition, prediction, machine learning, deep learning