Diplomová práce / info:eu-repo/semantics/masterThesis
Osoba oponující práci:
This diploma thesis is devoted to the analysis of the Long Short-Term Memory neural networks performance in prediction of randomly selected ﬁnancial time series from Yahoo!Finance. Firstly,we determined ﬁve most precise LSTM models used for predictions of 150 financial time series of stocks from various industries. Then we calculated the statistical measures related to the time series predictability - the Hurst Coefﬁcient, Metric Entropy and the largest Lyapunov Exponent. By estimation of the simple regression lines and the correlation coefﬁcients we try to determine possible relationship between the quality of prediction represented by the average RMSE and each of the statistics related to the time series predictability with the consequent comparison with the theoretical assumptions.
Python; Time Series; Hurst Coefficient; Long Short-Term Memory; Lyapunov Exponent; Forecast; Artificial Neural Networks; Sample Entropy