How to cite this paper
Chadidjah, A., Jaya, I & Kristiani, F. (2024). The comparison stateless and stateful LSTM architectures for short-term stock price forecasting.International Journal of Data and Network Science, 8(2), 689-698.
Refrences
Bernico, M. (2018). Deep Learning Quick Reference . Mumbai: Packt Publishing.
Bismi, I. (2023, May 29). Difference between Stateful and Stateless RNNs. [Accessed 06 January 2024]. From Medium: https://medium.com/@iqra.bismi/difference-between-stateful-and-stateless-rnns-2b397184e759
Brownlee, J. (2019, August 28). How To Backtest Machine Learning Models for Time Series Forecasting. Retrieved Janu-ary 2024, from Machine Learning Mastery: https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
business-science.io. (2018, April 17). Time Series Deep Learning: Forecasting Sunspots With Keras Stateful LSTM In R. [Accessed 06 January 2024]. From R-Bloggers: https://www.r-bloggers.com/2018/04/time-series-deep-learning-forecasting-sunspots-with-keras-stateful-lstm-in-r/
Dixon, M. F., Polson, N. G., & Sokolov, V. O. (2019). Deep learning for spatio‐temporal modeling: dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry, 35(3), 788-807.
Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algo-rithm. Expert Systems with Applications, 227, 120346.
Ho, M., Darman, H., & Musa, S. (2021). Stock Price Prediction Using ARIMA, Neural Network and LSTM Models. Jour-nal of Physics: Conference Series, 1988, 012041.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Lee, S., & Kim, H. (2020). Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation Author links open overlay panel. Expert Systems with Applications, 161(15), 113704.
Levin, E. (1990). A Recurrent Neural Network: Limitations and Training. Neural Networks, 3, 641-650.
Moghar, A., & Hamiche, M. (2020). Stock Market Prediction Using LSTM Recurrent Neural Network. Procedia Computer Science, 170, 1168-1173.
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M., Barrow, D., Taieb, S., . . . Bessa, R. (2022). Forecasting: the-ory and practice. International Journal of Forecasting, 38, 705-871.
Polyzos, S., Samitas, A., & Spyridou, A. E. (2021). Tourism demand and the COVID-19 pandemic: An LSTM approach. Tourism Recreation Research, 46(2), 175-187.
Ruiz-Cárdenas, R., Krainski, E., & Rue, H. (2012). Direct fitting of dynamic models using integrated nested Laplace ap-proximations — INLA. Computational Statistics and Data Analysis, 56, 1808–1828.
West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. New York: Springer.
Wu, J., Xu, K., Chen, X., Li , S., & Zhao, J. (2022). Price graphs: Utilizing the structural information of financial time se-ries for stock prediction. Information Sciences, 588, 405-424.
Bismi, I. (2023, May 29). Difference between Stateful and Stateless RNNs. [Accessed 06 January 2024]. From Medium: https://medium.com/@iqra.bismi/difference-between-stateful-and-stateless-rnns-2b397184e759
Brownlee, J. (2019, August 28). How To Backtest Machine Learning Models for Time Series Forecasting. Retrieved Janu-ary 2024, from Machine Learning Mastery: https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/
business-science.io. (2018, April 17). Time Series Deep Learning: Forecasting Sunspots With Keras Stateful LSTM In R. [Accessed 06 January 2024]. From R-Bloggers: https://www.r-bloggers.com/2018/04/time-series-deep-learning-forecasting-sunspots-with-keras-stateful-lstm-in-r/
Dixon, M. F., Polson, N. G., & Sokolov, V. O. (2019). Deep learning for spatio‐temporal modeling: dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry, 35(3), 788-807.
Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algo-rithm. Expert Systems with Applications, 227, 120346.
Ho, M., Darman, H., & Musa, S. (2021). Stock Price Prediction Using ARIMA, Neural Network and LSTM Models. Jour-nal of Physics: Conference Series, 1988, 012041.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Lee, S., & Kim, H. (2020). Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation Author links open overlay panel. Expert Systems with Applications, 161(15), 113704.
Levin, E. (1990). A Recurrent Neural Network: Limitations and Training. Neural Networks, 3, 641-650.
Moghar, A., & Hamiche, M. (2020). Stock Market Prediction Using LSTM Recurrent Neural Network. Procedia Computer Science, 170, 1168-1173.
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M., Barrow, D., Taieb, S., . . . Bessa, R. (2022). Forecasting: the-ory and practice. International Journal of Forecasting, 38, 705-871.
Polyzos, S., Samitas, A., & Spyridou, A. E. (2021). Tourism demand and the COVID-19 pandemic: An LSTM approach. Tourism Recreation Research, 46(2), 175-187.
Ruiz-Cárdenas, R., Krainski, E., & Rue, H. (2012). Direct fitting of dynamic models using integrated nested Laplace ap-proximations — INLA. Computational Statistics and Data Analysis, 56, 1808–1828.
West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. New York: Springer.
Wu, J., Xu, K., Chen, X., Li , S., & Zhao, J. (2022). Price graphs: Utilizing the structural information of financial time se-ries for stock prediction. Information Sciences, 588, 405-424.