Waveel: a wavelet-based ensemble learning framework for time series forecasting
Abstract With the booming development of the Internet of Things, billions of smart devices and sensors have generated massive amounts of time series data. Accurate time series forecasting (TSF) can help to uncover deep underlying patterns in the data. However, most existing TSF models focus on tempo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-025-02479-4 |
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| _version_ | 1849333969603002368 |
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| author | Xiaohou Shi Tianxin Hu Yuan Chang Yaqi Song Aobo Liang Yan Sun |
| author_facet | Xiaohou Shi Tianxin Hu Yuan Chang Yaqi Song Aobo Liang Yan Sun |
| author_sort | Xiaohou Shi |
| collection | DOAJ |
| description | Abstract With the booming development of the Internet of Things, billions of smart devices and sensors have generated massive amounts of time series data. Accurate time series forecasting (TSF) can help to uncover deep underlying patterns in the data. However, most existing TSF models focus on temporal features and ignore the rich frequency information in time series data. Additionally, due to the non-stationarity of real-world data, previous works face challenges of adapting to diverse data distributions across domains, leading to difficulties in ensuring consistent performance and generalizability in varying scenarios. To address these issues, we propose a wavelet-based ensemble learning framework WaveEL for TSF. WaveEL converts time series data into wavelet domain coefficients while capturing both temporal and frequency domain features. We further integrate multiple TSF models and propose a weight decision maker for determining the weight of model combinations in TSF tasks. We conduct extensive experiments on seven real-world datasets and the results show that our model exhibits superior prediction performance. |
| format | Article |
| id | doaj-art-e4a5f09cb17640c8879ee0dc421e251f |
| institution | Kabale University |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-e4a5f09cb17640c8879ee0dc421e251f2025-08-20T03:45:43ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-07-012025112010.1186/s13638-025-02479-4Waveel: a wavelet-based ensemble learning framework for time series forecastingXiaohou Shi0Tianxin Hu1Yuan Chang2Yaqi Song3Aobo Liang4Yan Sun5China Telecom Research InstituteComputer Science (National Pilot Software Engineering School), Beijing University of Posts and TelecommunicationsChina Telecom Research InstituteChina Telecom Research InstituteComputer Science (National Pilot Software Engineering School), Beijing University of Posts and TelecommunicationsComputer Science (National Pilot Software Engineering School), Beijing University of Posts and TelecommunicationsAbstract With the booming development of the Internet of Things, billions of smart devices and sensors have generated massive amounts of time series data. Accurate time series forecasting (TSF) can help to uncover deep underlying patterns in the data. However, most existing TSF models focus on temporal features and ignore the rich frequency information in time series data. Additionally, due to the non-stationarity of real-world data, previous works face challenges of adapting to diverse data distributions across domains, leading to difficulties in ensuring consistent performance and generalizability in varying scenarios. To address these issues, we propose a wavelet-based ensemble learning framework WaveEL for TSF. WaveEL converts time series data into wavelet domain coefficients while capturing both temporal and frequency domain features. We further integrate multiple TSF models and propose a weight decision maker for determining the weight of model combinations in TSF tasks. We conduct extensive experiments on seven real-world datasets and the results show that our model exhibits superior prediction performance.https://doi.org/10.1186/s13638-025-02479-4Time series forecastingDiscrete wavelet transformEnsemble learning |
| spellingShingle | Xiaohou Shi Tianxin Hu Yuan Chang Yaqi Song Aobo Liang Yan Sun Waveel: a wavelet-based ensemble learning framework for time series forecasting EURASIP Journal on Wireless Communications and Networking Time series forecasting Discrete wavelet transform Ensemble learning |
| title | Waveel: a wavelet-based ensemble learning framework for time series forecasting |
| title_full | Waveel: a wavelet-based ensemble learning framework for time series forecasting |
| title_fullStr | Waveel: a wavelet-based ensemble learning framework for time series forecasting |
| title_full_unstemmed | Waveel: a wavelet-based ensemble learning framework for time series forecasting |
| title_short | Waveel: a wavelet-based ensemble learning framework for time series forecasting |
| title_sort | waveel a wavelet based ensemble learning framework for time series forecasting |
| topic | Time series forecasting Discrete wavelet transform Ensemble learning |
| url | https://doi.org/10.1186/s13638-025-02479-4 |
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