The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Optimizing LSTM for time series prediction in Indian stock market
Open accessAbstract
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.
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Cites
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Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Deep learning with long short-term memory networks for financial market predictions
Fischer, T., & Krauss, C. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.11.054 · Cited by 2,785
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Random search for hyper-parameter optimization
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NSE Stock Market Prediction Using Deep-Learning Models
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Financial time series forecasting using independent component analysis and support vector regression
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How many hidden layers and nodes?
Stathakis, D. (2009) · International Journal of Remote Sensing · doi:10.1080/01431160802549278 · Cited by 432
Lam, M. (2004) · Decision Support Systems · doi:10.1016/s0167-9236(03)00088-5 · Cited by 380
An Efficient Approach for Assessing Hyperparameter Importance
Hutter, F., Hoos, H. H., & Leyton‐Brown, K. (2014) · Cited by 350
Metadata
- Authors
- Yadav, A., Jha, C. K., & Sharan, A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 167
- Pages
- 2091-2100
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W3016414288
- Cited by
- 365
- References
- 15
- FWCI
- 70.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science. https://doi.org/10.1016/j.procs.2020.03.257
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