Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Althelaya, K. A. , El-Alfy, E. -S. M. & Mohammed, S. (2018). Evaluation of bidirectional LSTM for short-and long-term stock market prediction. In 2018 9th international conference on information and communication systems (ICICS) pp. 151–156.
2. Bai, S., Kolter, J. Z. & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv: arxiv.org/abs/1803.01271
3. Chu, B., & Qureshi, S. (2023). Comparing out-of-sample performance of machine learning methods to forecast us GDP growth. Computational Economics, 62(4), 1567–1609.
4. Fang, Z., Ma, X., Pan, H., Yang, G., & Arce, G. R. (2023). Movement forecasting of financial time series based on adaptive LSTM-BN network. Expert Systems with Applications, 213, 119207.
5. Gajamannage, K., Park, Y., & Jayathilake, D. .I. (2023). Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMS. Expert Systems with Applications, 223, 119879.