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Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems [article]

Alexey Averkin, Sergey Yarushev
2020 arXiv   pre-print
In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets.  ...  This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.  ...  of the methodology and a software platform for the construction of digital twins, intellectual analysis and forecast of complex economic systems", grant no.  ... 
arXiv:2011.05588v1 fatcat:e5ef3h3aovaybgqfhi5qvphqnq

Forecasting and recombining time-series components by using neural networks

J V Hansen, R D Nelson
2003 Journal of the Operational Research Society  
The second assesses the use of stacked generalization as a way of refining this process. The methodology is applied to four economic and business time series.  ...  Stacked generalization first uses transformations and decomposition to pre-process a time series. Then a time-delay neural network receives the resulting components as inputs.  ... 
doi:10.1057/palgrave.jors.2601523 fatcat:a4up4yrhkzfk7hr43tml4movay

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Michael Ayitey Junior, Peter Appiahene, Obed Appiah
2022 Journal of Electrical Systems and Information Technology  
The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used.  ...  The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis  ...  Acknowledgements I would want to thank everyone who contributed in any way, as well as all of the researchers that supported me with my research; their research papers provided me with a lot of inspiration  ... 
doi:10.1186/s43067-022-00054-1 doaj:11995e80a2fd4f8e80cb371e0d9cfd3d fatcat:j7oz2lmnprgmtjfwogxt63kenm

A Review on Graph Neural Network Methods in Financial Applications [article]

Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song
2022 arXiv   pre-print
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks.  ...  In this work, we provide a comprehensive review of GNN models in recent financial context.  ...  Sequential numerical data Updating information as time goes, the financial industry has a rich source of time-series data.  ... 
arXiv:2111.15367v2 fatcat:o7gfnhlmrnetnl2s2sccjoquje

A Review on Graph Neural Network Methods in Financial Applications

Jianian Wang, Sheng Zhang, Yanghua Xiao, Rui Song
2022 Journal of Data Science  
Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks.  ...  In this work, we provide a comprehensive review of GNN models in recent financial context.  ...  Gcnext: Graph convolutional network with expanded balance theory for fraudulent user detection.  ... 
doi:10.6339/22-jds1047 fatcat:lpkkobcferal3p7l5wanydm7ay

Fault Diagnosis of Transmission Line using Feed Forward Neural Network

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The implementation of neural network for the fault diagnosis is to improve the dependability of the proposed scheme by providing a more accurate, faster diagnosis relaying scheme as compared with the conventional  ...  To achieve optimum result we have to improve following things: (i) Training time, (ii) Selection of training vector, (iii) Upgrading of trained neural nets and integration of technologies.  ...  Stack the arrangement of coefficients of the two aerial methods of a specific level over one another to make a vector used to prepare ANN. A neural system of proper size is utilized for preparing.  ... 
doi:10.35940/ijitee.i1054.0789s219 fatcat:lwzimhimqzc77olufobfmnolbu

Predicting Stock Closing Prices in Emerging Markets with Transformer Neural Networks: The Saudi Stock Exchange Case

Nadeem Malibari, Iyad Katib, Rashid Mehmood
2021 International Journal of Advanced Computer Science and Applications  
The financial sector is no surprise where the use of deep learning has produced one of the most lucrative applications.  ...  This research proposes a novel fintech machine learning method that uses Transformer neural networks for stock price predictions.  ...  Using a stacked autoencoder and deep neural network, Takeuchi and Lee [17] obtains an accuracy of 53.36 % when predicting the US stock direction.  ... 
doi:10.14569/ijacsa.2021.01212106 fatcat:fybiddl7mbbrpepvdqappauify

Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction

Karanveer Singh, Rahul Tiwari, Prashant Johri, Ahmed A. Elngar
2020 Journal of Information Technology Management  
Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy.  ...  Our proposed new neural network model gives accuracy is closely 86%.  ...  Compliance with Ethical Standards Conflict of interest on behalf of all authors, the corresponding author states that there is no conflict of interest.  ... 
doi:10.22059/jitm.2020.79368 doaj:60ca8d00fff44df88d43047c11349022 fatcat:yed74xl4pzd3hirr63aeof53py

Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network

Xinpeng Yu, Dagang Li
2021 Applied Sciences  
Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system.  ...  Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important  ...  Unlike the common methods that use a "many-to-one" model with a sliding window of a fixed size of preceding data, this work adopts the "many-to-many" model; i.e., the overall historical time series is  ... 
doi:10.3390/app11093984 fatcat:bzzxwzxwlbaxbpj3sovnb3iuqi

Neural Networks and Value at Risk [article]

Alexander Arimond, Damian Borth, Andreas Hoepner, Michael Klawunn, Stefan Weisheit
2020 arXiv   pre-print
Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along  ...  Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming  ...  The views expressed in this manuscript are not necessarily shared by Sociovestix Labs, the Technical Expert Group of DG FISMA or Warburg Invest AG.  ... 
arXiv:2005.01686v2 fatcat:ou3pjdy3hrgbvpudhbv3xzbv7i

Recurrent neural network optimization for wind turbine condition prognosis

Kerboua Adlen, Kelaiaia Ridha
2022 Diagnostyka  
This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation's health from collected vibration time series data, by using several memory cell variations, including  ...  The obtained results show the effectiveness of the proposed method, which generates more accurate recurrent models with a more accurate prognosis of the operating state of the wind turbine than those generated  ...  The authors proposed a new method based on deep feature modelling and a LSTM neural network for predicting the remaining useful life of rolling bearings in [11] .  ... 
doi:10.29354/diag/151608 fatcat:3vjdqt63mnf4joc4eojmwmagse

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer's Disease Image Data Classification

Robert Logan, Brian G. Williams, Maria Ferreira da Silva, Akash Indani, Nicolas Schcolnicov, Anjali Ganguly, Sean J. Miller
2021 Frontiers in Aging Neuroscience  
Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in  ...  We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data.  ...  ACKNOWLEDGMENTS We would like to thank all members of the Pluripotent Diagnostics team for their fruitful conversations and the Pluripotent Diagnostics Corp. for funding this study.  ... 
doi:10.3389/fnagi.2021.720226 pmid:34483890 pmcid:PMC8416107 fatcat:3bvka3lkunesxhehbpgqctolta

Analysis of Various Network Traffic Classification Techniques

Shivam Puri, Sukhpreet Kaur
2021 CGC International Journal of Contemporary Technology and Research  
On the basis of observed attributed of an object within the system, another attributed is predicted using new model.  ...  The daily lives of individuals include social networking, making financial transactions, generating networks that show physical systems and so on.  ...  Time-series based features: In general, time-series data can be regarded as a series of episodes sequenced in time sequence.  ... 
doi:10.46860/cgcijctr.2021.12.31.261 fatcat:ipw5jidymzfh7f77tfumfizroq

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions [article]

Wilfredo Tovar
2020 arXiv   pre-print
This paper proposes the implementation of a generative adversarial network (GAN), which is composed by a bi-directional Long short-term memory (LSTM) and convolutional neural network(CNN) referred as Bi-LSTM-CNN  ...  The novelty of this proposed solution that distinct from previous solutions is that this paper introduced the concept of a hybrid system (Bi-LSTM-CNN) rather than a sole LSTM model.  ...  Trend Models of Structural Time Series Models Generative-Adversarial Networks.  ... 
arXiv:2008.08041v2 fatcat:miwtpgsabrfvpgpkifqs4dp7mu

Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks

Huajin Li
2022 Frontiers in Energy Research  
First, the wind power time-series data from the target turbine and adjacent neighboring turbines were utilized to form a graph structure using graph neural networks (GNN).  ...  Then, the prediction models were trained using a deep residual network (DRN) for short-term wind power prediction.  ...  METHODOLOGY Graph Neural Network As we all know, a graph is a kind of structured data, which comprises a series of objects (nodes) and relationship types (edges) (Scarselli et al., 2008) .  ... 
doi:10.3389/fenrg.2022.920407 fatcat:pnzss7y6tbfohhvnafequbk6pa
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