Author:
Singh N,Sugandha ,Mathur T,Agarwal S,Tiwari K
Abstract
Abstract
Deep Learning is considered one of the most effective strategies used by hedge funds to maximize profits. But Deep Neural Networks (DNN) lack theoretical analysis of memory exploitation. Some traditional time series methods such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) work only when the entire series is pre-processed or when the whole data is available. Thus, it fails in a live trading system. So, there is a great need to develop techniques that give more accurate stock/index predictions. This study has exploited fractional-order derivatives’ memory property in the backpropagation of LSTM for stock predictions. As the history of previous stock prices plays a significant role in deciding the future price, fractional-order derivatives carry the past information along with itself. So, the use of Fractional-order derivatives with neural networks for this time series prediction is meaningful and helpful.
Subject
General Physics and Astronomy
Reference40 articles.
1. Stock price prediction using k-nearest neighbor (kNN) algorithm;Alkhatib;Int. J. of Business, Humanities and Technology,2013
2. Time series forecasting using a hybrid ARIMA and neural network model;Zhang;Neurocomputing,2003
3. LSTM: A search space Odyssey;Greff;IEEE Trans. Neural Netw. Learn Syst.,2016
4. Applications of deep learning in stock market prediction: recent progress;Jiang,2020
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献