Optimizing LSTM for time series prediction in Indian stock market

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.

[1]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[2]  Diyar Akay,et al.  Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting , 2009, Expert Syst. Appl..

[3]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[4]  Demetris Stathakis,et al.  How many hidden layers and nodes? , 2009 .

[5]  P. Dash,et al.  A hybrid stock trading framework integrating technical analysis with machine learning techniques , 2016 .

[6]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[7]  K. P. Soman,et al.  NSE Stock Market Prediction Using Deep-Learning Models , 2018 .

[8]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[9]  Lyle H. Ungar,et al.  Neural network forecasting of short, noisy time series , 1992 .

[10]  Diego Klabjan,et al.  Classification-Based Financial Markets Prediction Using Deep Neural Networks , 2016, Algorithmic Finance.

[11]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[12]  Erdogan Dogdu,et al.  A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters , 2017 .

[13]  Luca Di Persio,et al.  Artificial Neural Networks architectures for stock price prediction: comparisons and applications , 2016 .

[14]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.