Comparison of Two Partitioning Methods in a Fuzzy Time Series Model for Composite Index Forecasting
Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 4
Abstract
Study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improve forecasting accuracy. Recently, Fuzzy time-series based on Fibonacci sequence has been proposed as a new fuzzy time series model which incorporates the concept of the Fibonacci sequence, the framework of basic fuzzy time series model and the weighted method. However, the issue on lengths of intervals has not been investigated by the highly acclaimed model despite already affirmed that length of intervals could affects forecasting results. Therefore the purpose of this paper is to propose two methods of defining interval lengths into fuzzy time-series based on Fibonacci sequence model and compare their performances. Frequency density-based partitioning and randomly chosen lengths of interval partitioning were tested into fuzzy time-series based on Fibonacci sequence model using stock index data and compared their performances. A two-year weekly period of Kuala Lumpur Composite Index stock index data was employed as experimental data sets. The results show that the frequency density based partitioning outperforms the randomly chosen length of interval. This result reaffirms the importance of defining the appropriate interval lengths in fuzzy time series forecasting performances.
Authors and Affiliations
Lazim Abdullah , Yoke Ling
Keywords
Related Articles
- EP ID EP139822
- DOI -
- Views 113
- Downloads 0
On high order methods for solution of nonlinear equation
The objective of this paper is two folds, first to derive a new series of third order methods for solving non-linear equation f(x)=0 involving third, fourth and fifth derivatives of f and secondly to compare the existing...
AN ARTIFICIAL FISH SWARM OPTIMIZED FUZZY MRI IMAGE SEGMENTATION APPROACH FOR IMPROVING IDENTIFICATION OF BRAIN TUMOUR
In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consuming while carried out by...
A Survey on clustering Current status and challenging issues
Clustering is the art of subset from a dataset. It helps in identifying the hidden information and arranging data into its logical group based on an attribute or a set of attributes. The intent of this paper is to explor...
Simulator for Software Project Reliability Estimation
Several models are there for software development processes, each describing approaches to a variety of tasks or activities that take place during the process. Without project management, software projects can easily be...
AN EFFICIENT CLASSIFICATION OF GENOMES BASED ON CLASSES AND SUBCLASSES
The grass family has been the subject of intense research over the past. Reliable and fast classification / sub-classification of large sequences which are rapidly gaining importance due to genome sequencing projects all...