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

11/11/2020 ∙ by Alexey Averkin, et al. ∙ 0 ∙ share

In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. Also, we make short review of researches in forecasting based on various models of ANFIS models. Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally, we show possibility of using these models for data science tasks. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.

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Authors

Alexey Averkin
2 publications
Sergey Yarushev
2 publications

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

Authors

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

Decentralized Flood Forecasting Using Deep Neural Networks

Rethinking Representations in P C Actuarial Science with Deep Neural Networks

Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data

The effects of regularisation on RNN models for time series forecasting: Covid-19 as an example

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