A neural network forecasting model for integrated economic indicators

Gusev, Konstantin; Бурковский, В. Л.

doi:10.1134/s0005117913090129

Cited by 3 publications

(3 citation statements)

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“…The research uses a huge variety of different types of neural networks. Their regression variations were used for the purposes of this article (Gusev and Burkovskii, 2013;Yumashev et al, 2020).…”

Section: Ann Forecasting Model Descriptionmentioning

confidence: 99%

Multi-Source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Baboshkin

Uandykova

2021

IJEEP

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This article sheds light on the question of whether it is possible to create fairly accurate forecasts of real oil prices. For this purpose, a multi-level machine learning model has been created to analyze several sources of heterogeneous data to predict future prices. The article uses different types of data: market condition data, titles, and transaction data. Then, they have been processed to be able to load them into the model. The validation of the regression neural network results showed that the model is more accurate than in previous studies. In fact, this paper presents an artificial neural network model that solves the problem of determining the most informative relationship between different types of oil price data.

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Section: Ann Forecasting Model Descriptionmentioning

confidence: 99%

Multi-Source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Baboshkin

Uandykova

2021

IJEEP

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“…Especially in recent years, IT2 FLSs have been widely used on forecasting activities. Most recent studies on load forecasting show that IT2 FLSs (Khosravi et al, 2012; Khosravi and Nahavandi, 2014) have superiority approximation capability even better than nonparametric neural networks (Barbounis and Theocharis, 2007; Gao et al, 2014; Gusev and Burkovskii, 2013; Mehdi et al, 2016; Pany and Ghoshal, 2015). Furthermore, IT2 FLSs based on optimization algorithms outperform their T1 counterparts on forecasting.…”

Section: Introductionmentioning

confidence: 99%

Design of Begian–Melek–Mendel structure-based interval type-2 fuzzy logic systems optimized with backpropagation algorithms

Chen

2022

Transactions of the Institute of Measurement and Control

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As an emerging technology, the efficient and energy conserving process of permanent magnetic drive (PMD) presents high uncertainties. This paper designs a type of Begian–Melek–Mendel (BMM) structure interval type-2 fuzzy logic systems (IT2 FLSs) for PMD process uncertain parameters forecasting. The antecedent, consequent, and input measurements of systems are all selected as the Gaussian type-2 primary membership functions with uncertain standard deviations. Then the backpropagation algorithms are used to tune the parameters of IT2 FLSs. According to the Monte Carlo simulation studies and convergence analysis, the proposed IT2 FLSs are proved to be superior to two corresponding T1 FLSs in generalization ability.

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“…However, these low computational linear models have great limitations on nonlinear and seasonal problems affected by uncertainties (Wu and Mendel, 2007). In order to develop nonlinear methods for forecasting, fuzzy logic systems (FLSs) (Chen et al, 2016b; Mendel, 2001; Khosravi and Nahavandi, 2014; Wang and Chen, 2018) and neural networks (NNs) (Gusev and Burkovskii, 2013; Mehdi et al, 2016) based on artificial intelligence have been employed in recent years. Recent research report on load forecasting show that interval type-2 fuzzy logic systems (IT2 FLSs) (Khosravi et al, 2012) have superior approximation capability that are even better than the NNs.…”

Section: Introductionmentioning

confidence: 99%

Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

Chen

Wang

2019

Transactions of the Institute of Measurement and Control

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Much more attention has been focused on studying and applying general type-2 fuzzy logic systems (GT2 FLSs) in recent years. The paper designs a type of Mamdani GT2 FLS for studying forecasting problems based on the data of permanent magnetic drive (PMD) loss. During the system design process, we choose the primary membership functions (MFs) of antecedent, consequent and input measurement general type-2 fuzzy sets (GT2 FSs) as Gaussian type MFs with uncertain standard deviations. The corresponding vertical slices (secondary MFs) are chosen as the triangle MFs. All the parameters of Mamdani GT2 FLSs are optimized by the quantum particle swarm optimization (QPSO) algorithms. Noisy data of PMD loss are adopted for both training and testing the proposed FLSs forecasting approaches. Simulation studies and convergence analysis are employed to show the effectiveness and feasibility of the proposed GT2 FLSs forecasting methods compared with their T1 and IT2 counterparts.

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A neural network forecasting model for integrated economic indicators

Cited by 3 publications

References 0 publications

Multi-Source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Multi-Source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Design of Begian–Melek–Mendel structure-based interval type-2 fuzzy logic systems optimized with backpropagation algorithms

Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

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