Results 21 to 30 of about 34,857 (308)

PERAMALAN NILAI TUKAR PETANI SUBSEKTOR TANAMAN PANGAN PROVINSI BALI MENGGUNAKAN METODE FUZZY TIME SERIES CHEN

open access: yesE-Jurnal Matematika, 2023
Forecasting is a way to predict future events. One of the methods for forecasting is to use fuzzy time series Chen method. Fuzzy time series Chen is a development of fuzzy time series Song and Chissom method with more simplified arithmetic operations. In
ULYATIL AENI   +2 more
doaj   +1 more source

A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT

open access: yesJurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, 2018
Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather,
Bagus Dwi Saputra   +2 more
doaj   +1 more source

Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System

open access: yes, 2010
Stabilometry is a branch of medicine that studies balance-related human functions. The analysis of stabilometric-generated time series can be very useful to the diagnosis and treatment balance-related dysfunctions such as dizziness.
Caraça-Valente Hernández, Juan Pedro   +9 more
core   +2 more sources

Real-Time joint Landmark Recognition and Classifier Generation by an Evolving Fuzzy System. [PDF]

open access: yes, 2006
A new approach to real-time joint classification and classifier design is proposed in this paper. It is based on the recently developed evolving fuzzy system (EFS) method and is applied to mobile robotics.
Angelov, Plamen   +3 more
core   +1 more source

Fuzzy Supervised Multi-Period Time Series Forecasting

open access: yesCybernetics and Information Technologies, 2019
The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies
Ilieva Galina
doaj   +1 more source

Peramalan Data Kunjungan Wisatawan Mancanegara ke Indonesia menggunakan Fuzzy Time Series

open access: yesJEPIN (Jurnal Edukasi dan Penelitian Informatika), 2019
Wisatawan mancanegara memegang peranan penting terhadap pertumbuhan ekonomi dari sektor pariwisata. Untuk meningkatkan kunjungan wisatawan mancanegara perlu dilakukan pembangunan yang berkelanjutan pada sektor pariwisata. Pembangunan yang dilakukan harus
Indra Jiwana Thira   +4 more
doaj   +1 more source

Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

open access: yesEntropy, 2021
Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series.
Airton Monte Serrat Borin   +3 more
doaj   +1 more source

Mixed-Order Fuzzy Time Series Forecast

open access: yesMathematics
Fuzzy time series forecasting has gained significant attention for its accuracy, robustness, and interpretability, making it widely applicable in practical prediction tasks.
Hao Wu, Haiming Long, Jiancheng Jiang
doaj   +1 more source

An Exponential Autoregressive Time Series Model for Complex Data

open access: yesMathematics, 2023
In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique
Gholamreza Hesamian   +3 more
doaj   +1 more source

Fuzzy information granules in time series data [PDF]

open access: yes2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291), 2003
It is often desirable to summarize a set of time series through typical shapes in order to analyze them. The algorithm presented here compares pieces of different time series in order to find similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to consider such subsets belonging to typical shapes with a degree of ...
Ortolani, Marco   +4 more
openaire   +2 more sources

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