Štruktúra kurzu
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- Introduction to the theory of time series modeling
- Classical methods for time series prediction (space & frequency domain, spectral analysis, autocorrelation, ARIMA models etc.)
- Hands-on example (pandas, basic characteristics, simple prediction)
- Machine learning for time series prediction (state-space methods, Hidden Markov Chain, Kalman filter, classical neural networks, recurrent networks, LSTM)
- Hands-on examples of machine learning methods (training set preparation for specific task and model, training process & evaluation)
- Complex example of time series prediction using recurrent neural network (temperature prediction from high-dimensional input data: training data set preparation, training process & validation, prediction with trained neural network)