Development of a Fuzzy Time Series Model Using Cat Swarm Optimization Clustering and Optimized Weights of Fuzzy Relations

Description

– Development of a Fuzzy Time Series Model Using Cat Swarm Optimization Clustering and Optimized Weights of Fuzzy Relations –

Download Development of a Fuzzy Time Series Model Using Cat Swarm Optimization Clustering and Optimized Weights of Fuzzy Relations. Computer Engineering students who are writing their projects can get this material to aid their research work.

Abstract

This research developed a hybrid forecasting technique that integrates Cat Swarm Optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) algorithms with Fuzzy Time Series (FTS) forecasting models.

Cat Swarm Optimization Clustering (CSO-C) which is an algorithm for data classification is adopted at the fuzzification stage to objectively partition the universe of discourse into unequal intervals.

Then, disambiguated fuzzy relationships are obtained using Fuzzy Set Grouping (FSG). Finally, Particle Swarm Optimization (PSO) was adopted to optimize the defuzzification phase; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from a fuzzy set group (FSG). The clustering and optimization algorithms were implemented in MATLAB.

Introduction

Fuzzy time series (FTS) techniques are utilized in the fields of science, engineering and general applications to develop prediction models for weather forecasting, predictive control, signal processing, population forecasting, enrolment and finance among others (Panagiotakis et al., 2016).

Forecasting can be defined as the prediction of what is going to happen in the future. Researchers are of the opinion that regardless of the technique used, there can never be a perfect forecast.

Meanwhile, the aim of forecasting is either to develop a prediction model that will lead to a more accurate forecasting result or an error reduced result compared to the ones in literature. There are three classes of forecasting methods namely; qualitative, quantitative and causal (Singh, 2016).

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