Autors: Koparanov, K. A., Georgiev, K. K., Shterev, V. A.
Title: Lookback period, epochs and hidden states effect on time series prediction using a LSTM based neural network
Keywords: Deep Learning; LSTM; RNN; Stock Price Predicting; Time Series

Abstract: Forecasting time series problem occurs in various subject areas. Recently neural network techniques have been used for solving such tasks. However, they have not been sufficiently studied. The article explores the influence of the lookback period, the training epochs, and hidden state dimensionality in forecasting time series using long short-term memory. Numerical experiments with example financial data show that using more lags does not improve the results. Such a study of model parameters is important for their proper selection.



    Proc. 28-th National Conference with International Participation "Telecom 2020", October 29 - 30, 2020, Sofia, Bulgaria, pp. 61-64, 2020, Bulgaria, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/TELECOM50385.2020.9299551

    Цитирания (Citation/s):
    1. D. Ageng, C. -Y. Huang and R. -G. Cheng, "A Short-Term Household Load Forecasting Framework Using LSTM and Data Preparation," in IEEE Access, vol. 9, pp. 167911-167919, 2021, doi: 10.1109/ACCESS.2021.3133702. - 2021 - в издания, индексирани в Scopus или Web of Science
    2. Angelis, G.-F., Timplalexis, C., Krinidis, S., Ioannidis, D., Tzovaras, D., NILM applications: Literature review of learning approaches, recent developments and challenges, Energy and Buildings 261,111951, DOI 10.1016/j.enbuild.2022.111951 - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus