Interpolation of Stock market Data with Fuzzy Conception Using Weka Tool

Year 2017, Volume: 8 Issue: 2, 27 - 33, 14.07.2017

Priti Choudhary Vinod Rampure

Abstract

Progressing growth of IT has brought rapid technological advancement. Technologies are getting advance at an exponential rate and hence massive amount of data is emerging at very enormous rate in different sector. So there are lots of baselines for researcher to roadmap their strategy for technological improvement. Huge amount of data i.e. terabytes of data are carried over computer networks to and from organization working in the field of business, engineering and science. Many approaches based on mathematical model were suggested for dredging association rule but they were complex for users. Our work contemplated an algorithm for interpolating Stock Market data using fuzzy data dredging through which fuzzy association rule can be induced for Stock series. Our work proposes the algorithm in which each fuzzy item has its own predefined minimum support count. Time series data can be any sequence data which has some trend or pattern in it. It may be either stock market data, climatic observed data, data observed from medical equipments. Our work also measures the data dispersion in time series data i.e. stock market data used here. It shows the deviation of the stock prices from the mean of stock price data points taken over a period of time which help the investors to decide whether to buy or sell their shares or products. Risk associated with particular share can also be predicted by understanding the obtained curve in the experiment. We have implemented the contemplated work in WEKA tool to get more accurate and efficient result along with visualization. Basically we are predicting how data are interpreted and predicted with accuracy in stock market using this effective tool.

Keywords

WEKA, fuzzy association rule

References

• Jiawei Han, Jian Pei, ―Data Mining Concepts and Techniques, ISBN-978-93-80931- 91-3, Elsevier 2013 [Google Scholar]
• Abdullah Al Mueen, ―Exact Primitives for Time Series Data Mining University of California riverside 2014 [Google Scholar]
• B.Liu, W.Hsu, YMa, ―Mining Association Rules with Multiple Minimum Supports, International Conference on Knowledge Discovery and Data Mining, pp-337-341 199 [Google Scholar]
• R.Agrawal, R.Srikant,‖Fast Algorithm for Mining Association Rules, International Conference on Very large Databases, pp -487-499, 1994 [Google Scholar]
• R.Agrawal, R.Srikant, ―Mining Sequential Patterns, The 11th International Conference On Data Engineering, pp-3-14, 1995 [Google Scholar]
• J-S.R.Jang, C-T.Sun, ―Neuro-Fuzzy and Soft Computing, ISBN-978-81-203-2243-1, PHI, 2011 [Google Scholar]
• T P Hong, K Y Lin and S L Wang, ―Fuzzy Data Mining for Interesting Generalized Association Rules, Fuzzy Sets & Symbols, Elsevier pp-255-269 2002 [Google Scholar]
• Cai, ―Mining Association Rules with Weighted Items International Database Engineering and Applications Symposium 1998 [Google Scholar]
• T P Hong, K Y Lin, ―Induction of Fuzzy Rules and membership Functions from Training Examples, Fuzzy Sets and Systems, Elsevier pp-33-47, 1996 [Google Scholar]
• Richard J.Povinelli, ―Time Series Data Mining: Identifying Temporal patterns for Characterization and prediction of Time series and Events Doctoral Thesis, Marquette University 1999 [Google Scholar]
• Christopher J. Neely, ―Technical Analysis in the Foreign Exchange Market: A Layman’s Guide A Review, 1997 [Google Scholar]
• R.Srikant, R.Agrawal, ―Mining Quantitative Association Rules in Large Relational Tables, International Conference on Management of Data, ACM pp-1-12, 199 47 [Google Scholar]
• Au, Chan, ―Mining Fuzzy Rules for Time Series Classification‖ International conference on Fuzzy system 2004 [Google Scholar]
• G. Das, K Lin, ―Rule Discovery from Time Series, in: Proceedings of the 4th International Conference on knowledge discovery and data mining pp-16-22 1998 [Google Scholar]