Cited By
View all- Nikolov V(2011)Local weighted approach to time series forecastingProceedings of the 12th International Conference on Computer Systems and Technologies10.1145/2023607.2023634(155-160)Online publication date: 16-Jun-2011
Optimizations in time series clustering and prediction
Pages 528 - 533
Abstract
In this paper a combination of time series clustering and prediction is considered. Both clustering and prediction are done by neural networks with supervised and unsupervised learning respectively. Some optimizations of the clustering procedure are proposed for software implementation. Prediction results for univariate time series are shown and analysed.
References
[1]
Antonov, A. Nikolov, V. 2009. Decision Making System for Clustering of Spread Curves. International conference Automatics and Informatics'09 Bulgaria, Sofia 29.09-4.14.2009, V-9--V-12.
[2]
Baretto, G. Time Series Prediction with the Self-Organizing Map: A Review. Department of Teleinformatics Engineering, Federal University of Cear'a Av. Mister Hull.
[3]
Crone S. Business Forecasting with Artificial Neural Networks. IBF Tutorial -- Institute of Business Forecasting, Boston, 2004.
[4]
Dave Touretzky. Neural Networks for Time Series Prediction. Course 15-486/782: Artificial Neural Networks Fall, 2006.
[5]
Kohonen, T. Self-Organizing Maps. Springer, 2001.
[6]
Kovacheva, Ts. Cluster Analysis. Information technologies and control, Sofia, No. 3, 2004, 24--30.
[7]
Nikolov, V. Clustering and Prediction of Risk Spread Curves. CompSysTech'09, Ruse, 2009.
[8]
Steve Lawrence, Ah Chung Tsoi, Andrew D. Back. Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness. Australian Conference on Neural Networks, ACNN'96. Australian National University, pp. 16--21, 1996.
[9]
Thome, A. Tenorio, M. A Selective Committee Architecture for Time Series Prediction and Pattern Classification. Purdue University, 1993.
[10]
Xu, R. Wunsch, D. Clustering (IEEE Press Series on Computational Intelligence), 2009.
[11]
Zhang, G. Neural Networks in Business Forecasting. Idea Group Publishing, 2004.
Recommendations
Software failure time series prediction with RBF, GRNN, and LSTM neural networks
AbstractThe important task of software quality assurance is failure prediction. Time series forecasting methods can be successfully used for this purpose. This paper aims to study and compare the effectiveness of software failure prediction using ...
Chaotic time series prediction by artificial neural networks
In this paper, we used four types of artificial neural network (ANN) to predict the behavior of chaotic time series. Each neural network that used in this paper acts as global model to predict the future behavior of time series. Prediction process is based ...
Multistep-Ahead Prediction of Time Series Using Independent Models
ICEICE '12: Proceedings of the 2012 Second International Conference on Electric Information and Control Engineering - Volume 02Multi step-ahead prediction of time series is widely used in real applications, such as finance, transportation and electrical load. A multi step-ahead independent prediction approach of time series is proposed. The step-by-step recurrent approach and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2010 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 June 2010
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
CompSysTech '10
Acceptance Rates
Overall Acceptance Rate 241 of 492 submissions, 49%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 194Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)1
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
Cited By
View all- Nikolov V(2011)Local weighted approach to time series forecastingProceedings of the 12th International Conference on Computer Systems and Technologies10.1145/2023607.2023634(155-160)Online publication date: 16-Jun-2011
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in