Optimizing LSTM for time series prediction in Indian stock market
| Autor: | Anita Yadav, C. K. Jha, Aditi Sharan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Order (exchange) 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Stock market Artificial intelligence Time series business computer General Environmental Science |
| Zdroj: | Procedia Computer Science. 167:2091-2100 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.03.257 |
| Popis: | Long Short Term Memory (LSTM) is among the most popular deep learning models used today. It is also being applied to time series prediction which is a particularly hard problem to solve due to the presence of long term trend, seasonal and cyclical fluctuations and random noise. The performance of LSTM is highly dependent on choice of several hyper-parameters which need to be chosen very carefully, in order to get good results. Being a relatively new model, there are no established guidelines for configuring LSTM. In this paper this research gap was addressed. A dataset was created from the Indian stock market and an LSTM model was developed for it. It was then optimized by comparing stateless and stateful models and by tuning for the number of hidden layers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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