Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer

Kent, Daniel; Salem, Fathi M.

doi:10.1109/mwscas.2019.8885035

Cited by 24 publications

(15 citation statements)

References 11 publications

(17 reference statements)

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“…Errors are stored and used by LSTMs in back propagation process. Decisions can be made by LSTM, such as what to keep and when to authorize reads [10,35].…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Kilimci¹,

Duvar²

2020

IEEE Access

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To forecast the movement directions of stocks, exchange rates, and stock markets are significant and an active research area for investors, analysts, and researchers. In this paper, word embedding and deep learning-based direction prediction of Istanbul Stock Exchange (BIST 100) is proposed by analyzing nine banking stocks with high volume in BIST 100. Though English news articles have been employed for forecasting of market direction previously, to the best of our knowledge, Turkish news articles and user comments from social media and different platforms have not been utilized with the combination of deep learning techniques and word embedding methods to predict the direction of Turkish stocks and market. For this objective, long short-term memory networks, recurrent neural networks, convolutional neural networks as deep learning algorithms and Word2Vec, GloVe, and FastText as word embedding models are evaluated. To demonstrate the effectiveness of proposed model, four different sources of Turkish news are collected. The news articles about stocks from Public Disclosure Platform (KAP), text-based technical analysis of each stock from Bigpara, user comments from both Twitter and Mynet Finans platforms are gathered. Experiment results demonstrate that the combination of deep learning techniques and word embedding methods have a great potential to predict the direction of BIST 100. INDEX TERMS Deep learning techniques, financial sentiment analysis, stock market prediction, word embedding methods This work is licensed under a Creative Commons Attribution 4.

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“…Errors are stored and used by LSTMs in back propagation process. Decisions can be made by LSTM, such as what to keep and when to authorize reads [10,35].…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

Kilimci¹,

Duvar²

2020

IEEE Access

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“…Errors are stored and used by LSTMs in the back-propagation process. Decisions can be made by LSTM, such as what to keep and when to authorize reads [21,22]. An LSTM network calculates network activations from the data array from the input layer to be transmitted to the data array of the output layer as follows:…”

Section: Deep Learning Modelsmentioning

confidence: 99%

US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Yasar¹,

Kilimci

2020

Symmetry

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Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

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“…LSTMs are put forward as a solution of vanishing or exploding issues of RNNs. Besides, LSTMs are also known as customized version of RNNs [19][20][21]. Essentially, LSTMs are improved in order to attain long-term dependencies that is the missing side of RNNs.…”

Section: Long Short-term Memory Network (Lstms)mentioning

confidence: 99%

Stock Pattern Classification from Charts using Deep Learning Algorithms

Kaya¹,

Yılmaz²,

Uzun³

et al. 2020

acperpro

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Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer

Cited by 24 publications

References 11 publications

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

An Efficient Word Embedding and Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using Twitter and Financial News Sites: A Case of Istanbul Stock Exchange (BIST 100)

US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Stock Pattern Classification from Charts using Deep Learning Algorithms

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