LSTM TARMOQLARINING MOHIYATI VA MOLIYAVIY PROGNOZLASHDAGI AHAMIYATI
##semicolon##
Kalit soʻzlar: LSTM, moliyaviy prognozlash, vaqt seriyalari, uzoq muddatli xotira, sunʼiy neyron tarmoq, deep learning, ketma-ketlik, overfitting, real vaqtli prognozlash, hybrid model##article.abstract##
Annotatsiya
Ushbu maqolada LSTM (Long Short-Term Memory) tarmoqlarining mohiyati
va ularning moliyaviy prognozlashdagi qoʻllanilishi haqida tushuncha berilgan. LSTM
tarmoqlarining vaqt boʻyicha ketma-ketlikdagi maʼlumotlarni samarali qayta ishlash
xususiyati moliyaviy prognozlashda yuqori natijadorlikka ega ekanligi tahlil qilinadi.
Shuningdek, bu tarmoqlarning imkoniyatlari va cheklovlari ham koʻrib chiqiladi.
##submission.citations##
Foydalanilgan Adabiyotlar
1. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural
Computation, 9(8), 1735-1780.
2. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget:
Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471.
3. Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv
preprint, arXiv:1308.0850.
4. Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). The performance of
LSTM and BiLSTM in forecasting time series. Proceedings of the IEEE
International Conference on Big Data.
5. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory
networks for financial market predictions. European Journal of Operational
Research, 270(2), 654-669.
6. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
7. Brownlee, J. (2017). Deep Learning for Time Series Forecasting: Predict the
Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
8. Wu, Y., & Tan, H. (2016). Short-term load forecasting based on LSTM recurrent
neural network. IEEE International Conference on Control and Automation.
9. Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long short term memory
networks for anomaly detection in time series. Proceedings of the European
Symposium on Artificial Neural Networks, Computational Intelligence and
Machine Learning.
10. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of
gated recurrent neural networks on sequence modeling. arXiv preprint,
arXiv:1412.3555.
