MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||7|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
Time series classification based on arima and adaboost
1 Key Laboratory of Intelligence Computing and Novel Software Technology
2 Tianjin University of Technology, TianJin, China 300384
* Corresponding author: Csr_dsp@sina.com
In this paper, a novel time series classification approach, which using auto regressive integrated moving average model (ARIMA) features and Adaptive Boosting (AdaBoost) classifications. ARIMA is particularly suitable for distinguishing time series signal and Adaboost is suitable for features classification. The simulation results have shown that the algorithm is feasible. And this method is more accurate than many existing method in multiple time series problems.
Key words: Time series classification / ARIMA / AdaBoost
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.