Paper Info
Title
Fuzzy forecasting with DNA computing
Abstract
There are many forecasting techniques including: exponential smoothing, ARIMA model, GARCH model, neural networks and genetic algorithm, etc. Since financial time series may be influenced by many factors, conventional model based techniques and hard computing methods seem inadequate in the prediction. Those methods, however, have their drawbacks and advantages. In recent years, the innovation and improvement of forecasting techniques have caught more attention, and also provides indispensable information in decision-making process. In this paper, a new forecasting technique, named DNA forecasting, is developed. This may be of use to a nonlinear time series forecasting. The methods combined the mathematical, computational, and biological sciences. In the empirical study, we demonstrated a novel approach to forecast the exchange rates through DNA. The mean absolute forecasting accuracy method is defined and used in evaluating the performance of linguistic forecasting. The comparison with ARIMA model is also illustrated.
Year
DOI
Venue
2006
10.1007/11925903_25
DNA
Keywords
Field
DocType
mean absolute forecasting accuracy,fuzzy forecasting,linguistic forecasting,dna forecasting,dna computing,arima model,garch model,conventional model,forecasting technique,financial time series,new forecasting technique,nonlinear time series forecasting,decision making process,empirical study,genetic algorithm,exponential smoothing,neural network
Exponential smoothing,Technology forecasting,Time series,Computer science,Fuzzy logic,Autoregressive integrated moving average,Probabilistic forecasting,Artificial intelligence,Autoregressive conditional heteroskedasticity,Genetic algorithm,Machine learning,Distributed computing
Conference
Volume
ISSN
ISBN
4287
0302-9743
3-540-49024-8
Citations 
PageRank 
References 
5
0.52
11
Authors
4
Name
Order
Citations
PageRank
Don Jyh-Fu Jeng1334.36
Junzo Watada241184.53
Berlin Wu312315.28
Jui-Yu Wu4587.31