Time Series Analysis

Popis kurzu

This course is focused to time series prediction problem. We begin with examples of classical
methods for modeling and prediction of time series and we continue to more advanced methods
based on machine learning. We finish with a complex example of training time series model on
historical data using neural network and we evaluate its performance in predicting future.

This course is focused to time series prediction problem. We begin with examples of classical
methods for modeling and prediction of time series and we continue to more advanced methods
based on machine learning. We finish with a complex example of training time series model on
historical data using neural network and we evaluate its performance in predicting future.

Obsah kurzu

• 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)

Předpoklady

• Základní znalost programování v Pythonu


• Středoškolské znalosti lineární algebry, matematické analýzy a teorie pravděpodobnosti. Bude předpokládáno základní porozumění pojmům jako vektor, matice, vektorový prostor, pravděpodobnost, podmíněná pravděpodobnost, nezávislost náhodných jevů a znalost násobení matic a derivace funkcí.


• Vlastní laptop s předinstalovaným Dockerem.


• Znalosti strojového učení na úrovni kurzu Úvod do strojového učení.

Studijní materiály