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

2020/11/11 by Alexey Averkin, Averkin, Alexey, Sergey Yarushev +1
Computer Science · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.NE #econ.GN #q-fin.EC

paper · pdf · doi:10.48550/arxiv.2011.05588

arxiv created 2020/11/11 · arxiv updated 2020/11/12

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.

Related

• Forecasting with Neuro-Dynamic Programming
• Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey
• Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction
• Forecasting with Deep Learning
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey