IJBTT

The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part

	International Journal of Engineering Trends and Technology (IJETT)
	© 2020 by IJETT Journal
	Volume-68 Issue-12
	Year of Publication : 2020
	Authors : Samingun Handoyo, Ying-Ping Chen
	DOI :  10.14445/22315381/IJETT-V68I12P220

Citation 

MLA Style: Samingun Handoyo, Ying-Ping Chen. The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part International Journal of Engineering Trends and Technology 68.12(2020):118-122. 

APA Style:Samingun Handoyo, Ying-Ping Chen. The Developing of Fuzzy System for Multiple Time Series Forecasting with Generated Rule Bases and Optimized Consequence Part.  International Journal of Engineering Trends and Technology, 68(12), 118-122.

Abstract
The paper aims to build and implement a predictive model called a fuzzy system. The fuzzy rule bases component is generated by using the input-output data pairs. Its consequence part is optimized by using ordinary least squares. The initial structure model is needed to create the input-output data pairs based on the multiple time series. The rule bases are generated by using table lookup schema in which each input-output pairs has a contribution as a candidate rule. The obtaining rule base is modified to be an efficient one by optimizing its consequence part. As a case study is used, the 2-time series assumed which they have a causality effect. The data are the soybean price of both domestic and abroad. The developed fuzzy system is used in the forecasting of the domestic soybean price. The fuzzy system`s performance is very satisfying, assessed according to the R-squared and mean squared error of criteria.

Reference
[1] Z. Wei, et al. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease The American Journal of Human Genetics, 92(6) (2013) 1008-1012
[2] S. Regina, L. Suganthi, U K Sneha, S. Shwetha, Diabetic Retinopathy Classification Using Machine Learning Techniques International Journal of Engineering Trends and Technology 68(1) (2020) 51-56.
[3] J. Sadhasivam, A. Rathore, I. Bose, S. Bhattacharjee, S. Jayavel, A Survey of Machine Learning Algorithms International Journal of Engineering Trends and Technology 68(4)(2020) 64-71.
[4] I.N. Purwanto, A. Widodo, and S. Handoyo. System For Selection Starting Lineup Of A Football Players yy Using Analytical Hierarchy Process Journal of Theoretical & Applied Information Technology, , 97(1) (2018) 19-31.
[5] H. Kusdarwati and S. Handoyo. System for Prediction of Non- Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model Int J Elec & Comp Eng (IJECE), 8(4) (2018) 2327-2337.
[6] A. Widodo and S. Handoyo. The Classification Performance Using Logistic Regression and Support Vector Machine Journal of Theoretical & Applied Information Technology, 95(19) (2018) 5184-5193.
[7] W. H. Nugroho, S. Handoyo, and Y.J. Akri, An Influence of Measurement Scale of Predictor Variable on Logistic Regression Modeling and Learning Vector Quantization Modeling for Object Classification International Journal of Electrical and Computer Engineering. (IJECE), 8(1) (2018) 333-343
[8] J. Qiu, S.R. Jammalamadaka, and N. Ning, Multivariate time series analysis from a Bayesian machine learning perspective. Annals of Mathematics and Artificial Intelligence, 88(10) (2020) 1061-1082.
[9] S. Handoyo, and Marji, The Fuzzy Inference System with Least Square Optimization for Time Series Forecasting. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 7(3) (2018) 1015-1026.
[10] S. Handoyo, Marji, I.N. Purwanto, and F. Jie, The Fuzzy Inference System with Rule Bases Generated using the Fuzzy C-Means to Predict Regional Minimum Wage in Indonesia International J. of Opers. And Quant. Management (IJOQM) , 24(4) (2018) 277-292.
[11] A. Efendi S. Handoyo A.P.S. Prasojo, and Marji, The Implementation Of The Optimal Rule Bases Generated By Hybrid Fuzzy C-Mean And Particle Swarm Optimization Journal of Theoretical & Applied Information Technology, 97(16) (2019) 4453-4453.
[12] S. Handoyo, and. H. Kusdarwati, Implementation of Fuzzy Inference System for Classification of Dengue Fever on the Malang villages in IOP Conference Series: Materials Science and Engineering, 546( 5) (2019) 052026 IOP Publishing.
[13] M.A. Kacimi, O. Guenounou, L. Brikh, F. Yahiaoui, and N. Hadid, New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules. Engineering Applications of Artificial Intelligence, 2020, 89: 103417.
[14] H. Kusdarwati, and S. Handoyo, Modeling Threshold Liner in Transfer Function to Overcome Non Normality of the Errors In IOP Conference Series: Materials Science and Engineering, 546(5) (2019) 052039. IOP Publishing
[15] J. Zhao, S. Huang., Q. Huang, G. Leng, H. Wang., and P. Li, Watershed water-energy balance dynamics and their association with diverse influencing factors at multiple time scales. Science of The Total Environment, 711 (2020) 135189.
[16] S. Handoyo, The Multiple Time Series Modeling with Autoregressive Distributed Lag. Int. Conference on App. Analysis and Math. Modeling, ICAAMM 2019, Istambul-Turkey, (2020) 120-124.
[17] Lakshmana Phaneendra Maguluri, R. Ragupathy Comparative Analysis of Companies Stock Price Prediction Using Time Series Algorithm, International Journal of Engineering Trends and Technology 68(11) (2020) 9-15.
[18] S. Handoyo, A.P.S. Prasojo, Applied Fuzzy System with R Software (Sistem Fuzzy terapan dengan Software R). First Edition. Malang. UBPress. (2017) 81-135.

Keywords
fuzzy system, optimized rule bases, predictive model, times series forecasting.