Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks

Chen, Jou-Fan; Chen, Wei‐Lun; Huang, Chunping; Huang, Szu-Hao; Chen, An-Pin

doi:10.1109/ccbd.2016.027

Cited by 107 publications

(70 citation statements)

References 21 publications

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“…They reported GAF encoding methods were able to achieve competitive results in a series of baseline problems that include different domains such as medicine, entomology, engineering and astronomy. Furthermore, this method has been found to perform well compared with other time series encoding techniques in applications such as the classification of future trends of financial data [48].…”

Section: Time Series Imagingmentioning

confidence: 94%

Tool wear classification using time series imaging and deep learning

Martínez-Arellano

Terrazas

Ratchev

2019

Int J Adv Manuf Technol

155

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Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

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Section: Time Series Imagingmentioning

confidence: 94%

Tool wear classification using time series imaging and deep learning

Martínez-Arellano

Terrazas

Ratchev

2019

Int J Adv Manuf Technol

155

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“…Traders emotions about the market is driven by the news hence they make decision based on the factors such as Price Earnings (PE) ratio, consumer price index and other political or financial news. In [8] author proposed an approach to convert news articles into Paragraph Vector to obtain the distributed representations of news articles. This vector is used to study the time based effect on the opening prices of multiple companies based on the events using LSTM.…”

Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

“…The news feed which affect the buy or sell decision needs more efficient filtering mechanisms. Author proposed planar feature representation methods and deep convolutional neural networks for stock market prediction in [8].…”

Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

CNN based Stock Market Prediction

Guruprasad¹,

Chandramouli²

2020

IJEAT

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Abstract: Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parametersimpacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

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“…The dimension of the layers are reduced with Pooling layers to reduce computation and it can also be viewed as increasing the feature concentration. [11] shows the potential of convolutional neural network for finance stock prediction. 1-d convolutional network [12] is also used to predict the stock movement as a classification model with 1 day close, open, high, low, volume data.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

Rajaa

Sahoo

2019

Communications in Computer and Information Science

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Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.

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Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks

Cited by 107 publications

References 21 publications

Tool wear classification using time series imaging and deep learning

Tool wear classification using time series imaging and deep learning

CNN based Stock Market Prediction

Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

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