A new hybrid recurrent artificial neural network for time series forecasting

Eğrioğlu, Erol; Baş, Eren

doi:10.1007/s00521-022-07753-w

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…In this work, we propose a computational model that consists of two phases: For the first phase, we carried out clustering and prediction [31][32][33] of the selected time series using neural networks (NNs) [34][35][36]. We then integrated the results by using a hierarchical approach of type-1, interval type-2, and general type-2 fuzzy models (Figure 3).…”

Section: Proposed Methodsmentioning

confidence: 99%

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Ramírez,

Melin,

Castillo

2023

Axioms

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In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out.

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Section: Proposed Methodsmentioning

confidence: 99%

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Ramírez,

Melin,

Castillo

2023

Axioms

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“…Neural networks are frequently employed for prediction using a technique called back-propagation, which involves adjusting the weights of neurons to improve the accuracy of forecasts. A fuzzy machine (FS) is an AI system that combines computational techniques with decision-making methods based on sound judgment to detect subtle changes in datasets [7]. It involves a collection of indistinct components with limited degrees of potential, which make up imprecise data.…”

Section: Fig 1 Hidden-layer Feed Forward Neural Networkmentioning

confidence: 99%

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

Woong

2024

JMC

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Artificial neural networks and fuzzy structures have gained significant popularity in the last decade for time series forecasting. The objective is to conduct a performance comparison of various strategies to determine which ones are more effective for time series forecasting. The dataset provides instruction and evaluates forecasting models, utilizing artificial neural networks and fuzzy architectures. The observation evaluates the overall effectiveness of the forecasting models and the use of the root mean square error and means absolute error measures. This comparison analysis provides initial insights into the efficacy of artificial neural networks and fuzzy structures for predicting time series data. In predicting time series data, this study examines the precision of two renowned artificial intelligence systems, Neural Networks and Fuzzy structures. To evaluate the two algorithms, two distinct types of time series were utilized: a synthetic dataset consisting of 150 variables and a real-world dataset including 129 data points about monetary forecasts. The models' forecasting accuracy, training duration, and generalization abilities were compared. The findings validated that neural network surpassed fuzzy structures in all performance metrics when handling synthetic data. This research emphasizes the capabilities of artificial neural networks and fuzzy structures in addressing complicated forecasting problems. It demonstrates that both techniques may be utilized for predicting future time series values.

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“…Contemplating this aspect of risk management with a focus on the human factor, we present a model for clustering, classification, and prediction of indicators using intelligent computing methods that have proven to be effective in solving complex problems [10][11][12], primarily supervised [13,14] and unsupervised [15,16] neural networks (NNs) and Type-1 and Type-2 fuzzy inference systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

Ramírez,

Melin,

Castillo

2024

Axioms

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Economic risk is a probability that measures the possible alterations, as well as the uncertainty, generated by multiple internal or external factors. Sometimes it could cause the impossibility of guaranteeing the level of compliance with organizational goals and objectives, which is why for their administration they are frequently divided into multiple categories according to their consequences and impact. Global indicators are dynamic and sometimes the correlation is uncertain because they depend largely on a combination of economic, social, and environmental factors. Thus, our proposal consists of a model for prediction and classification of multivariate risk factors such as birth rate and population growth, among others, using multiple neural networks and General Type-2 fuzzy systems. The contribution is the proposal to integrate multiple variables of several time series using both supervised and unsupervised neural networks, and a generalized Type-2 fuzzy integration. Results show the advantages of utilizing the method for the fuzzy integration of multiple time series attributes, with which the user can then prevent future threats from the global environment that impact the scheduled compliance process.

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A new hybrid recurrent artificial neural network for time series forecasting

Cited by 12 publications

References 43 publications

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

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