Stock price prediction using LSTM, RNN and CNN-sliding window model
01 Sep 2017-pp 1643-1647
TL;DR: This work uses three different deep learning architectures for the price prediction of NSE listed companies and compares their performance and applies a sliding window approach for predicting future values on a short term basis.
Abstract: Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.
Citations
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TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
504 citations
Cites methods from "Stock price prediction using LSTM, ..."
...[85] Stocks of Infosys, TCS and CIPLA from NSE 2014 Price data RNN, LSTM and CNN Accuracy -...
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...[84] compared LSTM, RNN, CNN, MLP, whereas in [85] RNN, LSTM, CNN, Autoregressive Integrated Moving Average (ARIMA) were preferred, Lee and Yoo [86] compared 3 RNN models (SRNN, LSTM, GRU) for stock price prediction and then constructed a threshold based portfolio with selecting stocks according to the predictions and Li et....
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TL;DR: Four types of deep learning architectures are used i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available for day-wise closing price of two different stock markets.
317 citations
Cites result from "Stock price prediction using LSTM, ..."
...Comparison study of different DL models of stock market prediction has already been done as we can see in [1]....
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TL;DR: A review of recent works on deep learning models for stock market prediction by category the different data sources, various neural network structures, and common used evaluation metrics to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines.
Abstract: Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Based on the summary, we also highlight some future research directions in this topic.
221 citations
Cites background from "Stock price prediction using LSTM, ..."
...…Ding et al. (2014, 2015); Chen et al. (2017); Dingli & Fournier (2017); Huynh et al. (2017); Li & Tam (2017); Liu & Song (2017); Nelson et al. (2017); Selvin et al. (2017); Singh & Srivastava (2017); Sun et al. (2017); Vargas et al. (2017); Weng et al. (2017); Yang et al. (2017); Zhang et al.…...
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...…(Yolcu et al., 2013), DEM (Araújo, 2011), DMN (Zamora & Sossa, 2017), NARXT (Menezes Jr & Barreto, 2008), PELMNN (Asadi et al., 2012) Song et al. (2019) DNN DNN Convolutional Neural Network Type Dingli & Fournier (2017) CNN Logit, SVM Gunduz et al. (2017) CNN Logit Selvin et al. (2017) 1-...
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TL;DR: Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation and show that the models are efficient in predicting stock closing price.
218 citations
TL;DR: Ntakaris et al. as discussed by the authors developed a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities, which utilizes convolutional filters to capture the spatial structure of the LOBs as well as long short-term memory modules to capture longer time dependencies.
Abstract: We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the spatial structure of the LOBs as well as long short-term memory modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price prediction of limit order book data with machine learning methods,” J. Forecasting , vol. 37, no. 8, 852–866, 2018]. In a more realistic setting, we test our model by using one-year market quotes from the London Stock Exchange, and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments that were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a “black box” model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features that translate well to other instruments is an important property of our model, which has many other applications.
176 citations
References
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TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Abstract: Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
72,897 citations
"Stock price prediction using LSTM, ..." refers background or methods in this paper
...With the introduction of LSTM [20], the analysis of time dependent data become more efficient....
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...LSTM is a special kind of RNN, introduced in 1997 by Hochreiter and Schmidhuber [20] ....
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
46,982 citations
Additional excerpts
...Memory(LSTM), CNN(Convolutional Neural Network) etc [9]....
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Book•
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
38,208 citations
Book•
01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher:
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
19,748 citations
TL;DR: A review of the past 25 years of research into time series forecasting can be found in this paper, where the authors highlight results published in journals managed by the International Institute of Forecasters.
1,383 citations