Optimizing LSTM for time series prediction in Indian stock market
By Anita Yadav, C. Jha, Aditi Sharan. 2020
308 citations recorded by the source corpus.
Abstract 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.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/003015d46871279afde17c7ab9e39a6ed7bc3a27
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In Management Science and Operations Research
Citations in the corpus
- A CNN-LSTM-Based Model to Forecast Stock Prices (2020, background): “In recent years, it has also been adopted in the field of stock market forecasting [30–32].”