Abstract :
In recent years, usage of time series forecasting has been increasing day by day
for prediction like, share market, weather forecasting and data analysis. Forecasting
of Mackey Glass chaotic time series has been carried out in this paper. It is
considered that prediction of a chaotic time series system is a nonlinear, multivariable and multi-modal optimization problem. To get an optimum output of times
series, global optimization techniques are required in order to minimize the effect of
local optima. Application of recent evolutionary techniques have been considered as
pervasive technology for Optimization. In this paper, Fuzzy Logic System (FLS) deals
with non-linearity and generates the rule base from training data used for time series
forecasting. Further, application of five recent evolutionary techniques have been
considered for optimization like Genetic Algorithm (GA) and Gravitational Search
Algorithm Particle Swarm Optimization (GSA-PSO),. A comparison for bench mark
data of time series forecasting is done using above discussed techniques. it is observed
that GA performs better as compared to GSAPSO in both terms, i.e. accuracy and
time.
Keyword :
Fuzzy Logic System, Time Series Forecasting, Genetic Algorithm, Gravitational Search Algorithm, Particle Swarm Optimization