Analýza časových řad
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Vysoká škola báňská – Technická univerzita Ostrava
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Abstract
Abstract
This thesis presents a comprehensive study of time series analysis using deep neural networks.
The efficacy of these methods is compared against the existing solutions for time series analysis.
The implementation of these methods is done on a selected data set and their performance is
verified. The implementation of the methods is done through the use of open source libraries, such
as TensorFlow and Keras. Darts framework is used to test existing solutions. The results of the
comparison are discussed and conclusions are drawn from them. The overall findings suggest that
deep neural networks can be effectively used for time series analysis, with results comparable to
existing methods.
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Subject(s)
Time Series Analysis, Deep Neural Networks, Time Series Classification, Time Series Prediction, Time Analysis Metrics, RNN, LSTM, GRU, Time Series Framework List, Keras, Darts, KU-HAR Dataset, Yoga Dataset, Chile Wind Speed b08, Jena Dataset, Natural Gas Forecasting