Forecasting Meteorological Indicators Based on Neural Networks

Kan, V. A.; Alsova, Olga K.

doi:10.1109/sibircon56155.2022.10017124

Cited by 5 publications

(2 citation statements)

References 7 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

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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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“…Although ANNs are a powerful tool for processing an infinity of data, their success has been demonstrated in applications from different areas of knowledge [20,21]. Today, we can find different applications that consider ANNs to solve different problems in which they have been proven to be effective and precise [22].…”

Section: Neural Networkmentioning

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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Modular Perspective for Population and Gross National Income Time Series Prediction Using a Neural Network Model: A Case Study of OECD Member Countries

Ramirez,

Melin

2024

Studies in Computational Intelligence

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Forecasting Meteorological Indicators Based on Neural Networks

Cited by 5 publications

References 7 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 New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

Modular Perspective for Population and Gross National Income Time Series Prediction Using a Neural Network Model: A Case Study of OECD Member Countries

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