![]() We use it to denoise the input financial time series and then feed them into the deep learning framework. It is a widely used technique for filtering and mining single-dimensional signals. WT is considered to fix the noise feature of financial time series. Thus, we decide to use this model to predict the stock trends. Evidence has proved that it is more effective than the conventional RNN. In addition, it can solve the problem of a vanishing gradient by having the memory unit retain the time related information for an arbitrary amount of time. Unlike conventional RNN, it is well-suited to learn from experience to predict time series when there are time steps with arbitrary size. LSTM is a type of recurrent neural network (RNN), with feedback links attached to some layers of the network. ![]() The other two methods are incorporated to help increase predictive accuracy. As a result, the SAEs model can successfully learn invariant and abstract features. The unsupervised training of SAEs is done one AE at a time by minimizing the error between the output data and the input data. Specifically, it is a neural network consisting of multiple single layer autoencoders in which the output feature of each layer is wired to the inputs of the successive layer. SAEs is the main part of the model and is used to learn the deep features of financial time series in an unsupervised manner. The proposed model in this paper consists of three parts: wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM). Therefore, this paper contributes to this area and provides a novel model based on the stacked autoencoders approach to predict the stock market. However, regarding whether the stacked autoencoders method could be applied to financial market prediction, few efforts have been made to investigate this issue. Also, certain works use deep belief networks in financial market prediction, for example, Yoshihara et al. combine the neural tensor network and the deep convolutional neural network to predict the short-term and long-term influences of events on stock price movements. The relevant work on deep learning applied to finance has introduced the former two approaches into the research. Generally speaking, there are three main deep learning approaches widely used in studies: convolutional neural networks, deep belief networks and stacked autoencoders. However, this field still remains relatively unexplored. Considering the complexity of financial time series, combining deep learning with financial market prediction is regarded as one of the most charming topics. An improvement over traditional machine learning models, the new one can successfully model complex real-world data by extracting robust features that capture the relevant information and achieve even better performance than before. ![]() In the literature, however, a recent trend in the machine learning and pattern recognition communities considers that a deep nonlinear topology should be applied to time series prediction. During the past decades, machine learning models, such as Artificial Neural Networks (ANNs) and the Support Vector Regression (SVR), have been widely used to predict financial time series and gain high predictive accuracy. How to accurately predict stock movement is still an open question with respect to the economic and social organization of modern society. Stock market prediction is usually considered as one of the most challenging issues among time series predictions due to its noise and volatile features. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All data are available from figshare database (DOI: 10.6084/m9.figshare.5028110).įunding: This work is supported by National Natural Science Foundation of China (Grant Number: 7137203306, ). Received: DecemAccepted: JPublished: July 14, 2017Ĭopyright: © 2017 Bao et al. PLoS ONE 12(7):Įditor: Boris Podobnik, University of Rijeka, CROATIA Citation: Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory.
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