Optimization of markov weighted fuzzy time series forecasting using genetic algorithm (GA) AND particle Swarm Optimization (PSO)

Surono, Sugiyarto (2022) Optimization of markov weighted fuzzy time series forecasting using genetic algorithm (GA) AND particle Swarm Optimization (PSO). Emerging Science Journal, 6 (6). ISSN ISSN: 2610-9182

Text (HASIL CEK SIMILARITY)
hasil-Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (2).pdf - Accepted Version
Download (3MB)

Official URL: https://www.researchgate.net/publication/363692162...

Abstract

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on
developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of
FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic
relationships. The main challenge of the MWFTS method is the absence of standardized rules for
determining partition intervals. This study compares the MWFTS model to the partition methods
Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clusteringParticle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval
and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s
performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most
significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves
maximizing the interval length following the FKM procedure. The proposed method was applied to
Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM
partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While
the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition
method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was
still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning
technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of
30,638 was reduced to 24,863.

Item Type:	Artikel Umum
Subjects:	Q Science > QA Mathematics
Divisi / Prodi:	Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika)
Depositing User:	Dr Sugiyarto Surono
Date Deposited:	14 Jun 2023 01:54
Last Modified:	14 Jun 2023 01:54
URI:	http://eprints.uad.ac.id/id/eprint/43378

Actions (login required)

View Item