Time series classification based on arima and adaboost
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 classi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_03024.pdf |
| _version_ | 1818585556289847296 |
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| author | Wang Jinghui Tang Shugang |
| author_facet | Wang Jinghui Tang Shugang |
| author_sort | Wang Jinghui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T08:38:57Z |
| format | Article |
| id | doaj.art-83b00e50ac04423b801b9f9d7733edb8 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-16T08:38:57Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-83b00e50ac04423b801b9f9d7733edb82022-12-21T22:37:43ZengEDP SciencesMATEC Web of Conferences2261-236X2020-01-013090302410.1051/matecconf/202030903024matecconf_cscns2020_03024Time series classification based on arima and adaboostWang Jinghui0Tang Shugang1Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyIn 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.https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_03024.pdftime series classificationarimaadaboost |
| spellingShingle | Wang Jinghui Tang Shugang Time series classification based on arima and adaboost MATEC Web of Conferences time series classification arima adaboost |
| title | Time series classification based on arima and adaboost |
| title_full | Time series classification based on arima and adaboost |
| title_fullStr | Time series classification based on arima and adaboost |
| title_full_unstemmed | Time series classification based on arima and adaboost |
| title_short | Time series classification based on arima and adaboost |
| title_sort | time series classification based on arima and adaboost |
| topic | time series classification arima adaboost |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_03024.pdf |
| work_keys_str_mv | AT wangjinghui timeseriesclassificationbasedonarimaandadaboost AT tangshugang timeseriesclassificationbasedonarimaandadaboost |