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

TL;DR

This paper explores deep neural networks and neuro-fuzzy models for economic time series forecasting, reviews existing ANFIS models, and demonstrates their potential in data science applications.

Contribution

It introduces new deep learning and neuro-fuzzy models tailored for economic forecasting and discusses integrating rule-based methods into neural networks.

Findings

Deep learning achieves high accuracy in complex data prediction.

Neuro-fuzzy networks effectively model economic time series.

Models show promise for practical data science tasks.

Abstract

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.