IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 8 - Issue 10, October 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Target Projection Pursuit Feature Selection Quadratic Associative Classifier for Time Series Big Data Prediction

[Full Text]

 

AUTHOR(S)

A.Selvakumar, Dr.S.Prasath

 

KEYWORDS

Time series big data, feature selection, Targeted Interactive Projection Pursuit, Jaccard similarity, Quadratic Associative classifier, association rules, space complexity.

 

ABSTRACT

Big data is a collection of data that are large in size and growing exponentially with respect to time. A time series is a sequence of monitored data over time. The various methods have been developed in the time series analysis. But the accurate prediction was not performed with minimum time. In order to improve the prediction accuracy with minimum time, an efficient Targeted Interactive Projection Pursuit Feature Selection based Quadratic Associative Data Classification (TIPPFS-QADC) technique is introduced. Initially, the TIPPFS-QADC technique collects a large volume of data from the big dataset. The TIPPFS-QADC technique comprises the two processes namely feature selection and classification. The TIPPFS-QADC technique uses Targeted Interactive Projection Pursuit for performing the feature selection in a time series database for reducing the prediction time and space complexity. Targeted Projection Pursuit is a statistical technique used to explore the space of projections through manipulating the Jaccard similarity between the features. After performing the feature selection, Quadratic Associative Data Classification is carried out to predict the future results of time series data. Quadratic Associative Classifier (QAC) is a supervised learning model that uses association rules to separate the two or more classes. Through performing the classification, the time series data prediction is carried out with superior accuracy and lesser time consumption. Experimental evaluation of proposed TIPPFS-QADC technique and existing methods are carried out using big dataset. The results observation clearly shows that the proposed TIPPFS-QADC technique obtains higher prediction accuracy and minimum false positive rate, prediction time and space complexity.

 

REFERENCES

[1] Armando Segatori, Alessio Bechini, Pietro Ducange and Francesco Marcelloni, “A Distributed Fuzzy Associative Classifier for Big Data”, IEEE Transactions on Cybernetics, Volume 48, Issue 9, 2018, Pages 2656 – 2669
[2] R.Talavera-Llames, R.Pérez-Chacón, A.Troncoso, F.Martínez-Álvarez, “MV-kWNN: A novel multivariate and multi-output weighted nearest neighbours algorithm for big data time series forecasting”, Neurocomputing, Elsevier, 2019, Pages 1-40
[3] A.Galicia, J.F.Torres, F.Martínez-Álvarez, A.Troncoso, “A novel spark-based multi-step forecasting algorithm for big data time series”, Information Sciences, Elsevier, Volume 467, 2018, Pages 800-818
[4] Francisco Padillo, Jose Maria Luna and Sebastian Ventura, “Evaluating associative classification algorithms for Big Data”, Big Data Analytics, Springer, Volume 4, Issue 2, 2019, Pages 1-27
[5] Francisco J. Baldan and Jose M. Benıtez, “Distributed FastShapelet Transform: a Big Data time series classification algorithm”, Information Sciences, Elsevier, 2018, Pages 1-22
[6] Kejian Shi , Hongyang Qin , Chijun Sima , Sen Li , Lifeng Shen, Qianli Ma, “Dynamic Barycenter Averaging Kernel in RBF Networks for Time Series Classification”, IEEE Access, Volume 7, Pages 47564 – 47576
[7] Xiaochuan Sun, Tao Li, Qun Li , Yue Huang , Yingqi Li, “Deep belief echo-state network and its application to time series prediction”, Knowledge-Based Systems, Elsevier, Volume 130 , 2017, Pages 17–29
[8] A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, F. Martinez-Alvarez, “Multi-step forecasting for big data time series based on ensemble learning”, Knowledge-Based Systems, Elsevier, Volume 163, 2019, Pages 830–841
[9] Trong Hai Duong, Phi Hung Do, Sy Dzung Nguyen, Minh Hien Hoang, “ENSO-based tropical cyclone forecasting using CF-ANFIS”, Vietnam Journal of Computer Science, Springer, Volume 3, Issue 2, 2016, Pages 81–91
[10] Eduardo Soares, Pyramo Costa Jr, Bruno Costa and Daniel Leite, “Ensemble of evolving data clouds and fuzzy models for weather time series prediction”, Applied Soft Computing, Elsevier, Volume 64, 2018, Pages 445-453
[11] Jie Chen, Guo-Qiang Zeng, Wuneng Zhou, Wei Du, Kang-Di Lu, “Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization”, Energy Conversion and Management, Elsevier, Volume 165, 2018, Pages 681–695
[12] Jun-He Yang, Ching-Hsue Cheng, and Chia-Pan Chan, “A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method”, Computational Intelligence and Neuroscience, Hindawi , Volume 2017, November 2017, Pages 1-11
[13] Chih-Chiang Wei, “Conceptual weather environmental forecasting system for identifying potential failure of under-construction structures during typhoons”, Journal of Wind Engineering & Industrial Aerodynamics, Elsevier, Volume 168, 2017, Pages 48–59
[14] Michael S.Gashler and Stephen C.Ashmore, “Modeling time series data with deep Fourier neural networks”, Neurocomputing, Elsevier, Volume 188, 2016, Pages 3-11
[15] Z. Ghaemi , A. Alimohammadi , M. Farnaghi, “LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran”, Environmental Monitoring and Assessment, Springer, Volume 190, Issue 300, 2018, Pages 1-17
[16] Rohitash Chandra, Yew-Soon Ong, Chi-Keong Gohc, “Co-evolutionary multi-task learning for dynamic time series prediction”, Applied Soft Computing, Elsevier, Volume 70, 2018, Pages 576-589
[17] Gian Antonio Susto, Andrea Schirru , Simone Pampuri, Seán McLoone, “Supervised Aggregative Feature Extraction for Big Data Time Series Regression”, IEEE Transactions on Industrial Informatics , Volume 12 , Issue 3 , 2016, Pages 1243 - 1252
[18] Saeed Aghabozorgi, Teh Ying Wah, Tutut Herawan, Hamid A. Jalab, Mohammad Amin Shaygan, and Alireza Jalali, “A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique”, The Scientific World Journal, Hindawi Publishing Corporation, Volume 2014, March 2014, Pages 1-12
[19] Nazanin Asadi , Abdolreza Mirzaei , Ehsan Haghshenas, “Creating Discriminative Models for Time Series Classification and Clustering by HMM Ensembles”, IEEE Transactions on Cybernetics , Volume 46 , Issue 12 , 2016 , Pages 2899 – 2910
[20] Pauline Ong and Zarita Zainuddin, “Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction”, Applied Soft Computing, Elsevier, Volume 80, 2019, Pages 374-386