Deep Learning Based Forecasting in Stock Market with Big Data Analytics

Önde, Mehmet Ali; Koçer, Furkan; Şahingöz, Özgür Koray

doi:10.1109/ebbt.2019.8741818

Cited by 25 publications

(10 citation statements)

References 9 publications

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“…Therefore, there are many research works related to this application area, as described in Ref. 198 In addition, various architectures such as CNN, [124][125][126] DNN, 123 GRU, 122 or LSTM 118,119 have been used. Some authors make a comparison between some of these architectures, analyzing which one offers better results.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…The data set used in this study has been sourced from Kaggle, an online community of data scientists which provides financial data at no cost. Kaggle is a popular data provider that has been used in many of the research projects [4,42,84]. Specifically, this study uses the "Huge Stock Market Dataset" [62], which provides millions of historical data points across a broad range of instruments.…”

Section: Datamentioning

confidence: 99%

Cloud based evaluation of databases for stock market data

Singh

Martyr

Medland³

et al. 2022

J Cloud Comp

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About fifty years ago, the world’s first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.

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“…Bu yapıyı açıklamanın başka bir yolu, şu ana kadar neyin hesaplandığı hakkında veri toplayan "hafıza" taşımalarıdır. Şekil 2'de gösterilen diyagram tam bir ağa açılan RNN yapısını göstermektedir [17].…”

Section: Kullanılan Derin öğRenme Yaklaşımlarıunclassified

“…Eğitim sürecinde, bellekte saklanan verilerin şekillendirilmesi sağlanır. Bu şekilde, eski veriler bile yeni verilerle etkileşime girer ve daha etkili sonuçlar üreten bir ağ yapısı oluşturur [17]. GRU'da geçitli bir yineleme ünitesi, bir güncelleme portu ve bir sıfırlama kapısı kullanılır.…”

Section: şEkil 2 Rnn şEmasıunclassified

Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini

Koçer

Önde

Şahingöz

2020

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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ÖzSon yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve "Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir. Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir. Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç, pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin zaman serisi değerleri kullanıldı. Spesifik olarakta IBM hisse senedi ile test çalışmaları yapıldı. Deneysel sonuçlar, hisse senedine ait son 5 günlük işlem verilerinin girdi olarak kullanıldığı BLSTM modeliyle yapılan eğitimin %63,54 lük bir yönsel doğruluk değerine ulaşıldığını göstermektedir. AbstractIn recent years, due to the technological advances in computer hardware and enhancements in machine learning techniques, there are two hot research areas in problem solving, the use of "Big Data" and "Parallel Processing". Many real-world problems can be solved with the use of different Deep Learning algorithms, which can be realized in parallel with multicore computing devices such as GPUs. Deep learning models show great success in applications such as classification of raw data, regression analysis and estimation in time series. One of the most active application areas of these models is "the financial market" which aims a good estimation of stock prices in the exchange market. In this paper, it is aimed to forecast the short or long term future value by looking at the previous log data of the stock on the process of change in the market. In this study, a price forecasting system was developed by using 3 different deep learning models named LSTM, GRU and BLSTM with a comparative analysis between them. The time series values of the stock were used from the New York Stock Exchange from 1968 to 2018 as a set of data to be free of speculative movements. Specifically, tests were conducted on IBM stock. Experimental results show that the directional accuracy of 63.54% was achieved with the BLSTM model where the last 5-day transaction data of the stock were used as input.

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Deep Learning Based Forecasting in Stock Market with Big Data Analytics

Cited by 25 publications

References 9 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Cloud based evaluation of databases for stock market data

Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini

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