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DDTCN: Decomposed dimension time-domain convolutional neural network along spatial dimensions for multiple long-term series forecasting

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Abstract

Time series analysis is widely applied in action recognition, anomaly detection, and weather forecasting. Time series forecasting remains a key challenge due to the complexity of temporal patterns, overlap** changes within sequences, and the need for advanced predictive models to forecast longer sequences in many scenarios. In this study, a model named the decomposed dimension time-domain convolutional neural network (DDTCN) is proposed. This model is specifically designed to address the challenges associated with long time series data. This paper presents a dimension temporal convolutional network (DTCN) module, which has a strong ability to capture variable correlations, and an adaptive strategy is introduced. Specifically, the model proposed in this paper first decomposes time series trends and is combined with the DTCN to extract variable correlations, thus achieving accurate predictions for complex time series and providing a powerful solution for long-term series forecasting. Experiments are conducted on multiple long-term series datasets covering five practical applications: energy, transportation, economics, weather, and health care . The proposed model is extensively evaluated and compared with traditional time series prediction methods and several benchmarks. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in most tasks involving multiple long-term series forecasting. Additionally, a pig price dataset is generated to predict agricultural economic trends, where compared to that of state-of-the-art methods, the DDTCN achieves a reduction in prediction error of 25.36%. Hence, this model holds promising prospects for wide-ranging applications.

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Data availability and access

We use real-world datasets collected by [17, 48]. The other representative datasets and codes can be found at https://github.com/1zkh/DDTCN.

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Acknowledgements

This work is supported in part by the Scientific Research Platforms and Projects of Guangdong Provincial Education Department (2021ZDZX1078; 2023ZDZX4002) and the Guangzhou Key Laboratory of Intelligent Agriculture (201902010081). The authors thank all the editors and reviewers for their suggestions and comments.

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Wushan, Guangzhou, Guangdong, 510642, People’s Republic of China

    Kaihong Zheng, **feng Wang & Yunqiang Chen

  2. Key Laboratory of Smart Agriculture of Guangzhou, Wushan, Guangzhou, Guangdong, 510642, People’s Republic of China

    **feng Wang

  3. Department of Digital Process, Guangdong Wens Food Group Co., Ltd., **nxing, Yunfu, Guangdong, 527300, People’s Republic of China

    Rong** Jiang

  4. College of Economics and Management, South China Agricultural University, Wushan, Guangzhou, Guangdong, 510642, People’s Republic of China

    Wenzhong Wang

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Contributions

Conceptualization, Kaihong Zheng and **feng Wang; methodology, Kaihong Zheng and **feng Wang; software, Kaihong Zheng; validation, Kaihong Zheng and **feng Wang; formal analysis, **feng Wang and Wenzhong Wang; investigation, Kaihong Zheng; resources, **feng Wang and Rong** Jiang and Wenzhong Wang; data curation, Kaihong Zheng and Yunqiang Chen; writing—original draft preparation, Kaihong Zheng; writing—review and editing, **feng Wang; visualization, Kaihong Zheng; Supervision, **feng Wang; project administration, **feng Wang; funding acquisition, **feng Wang. All authors have read and agreed to the published version of the manuscript.

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Correspondence to **feng Wang.

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Zheng, K., Wang, J., Chen, Y. et al. DDTCN: Decomposed dimension time-domain convolutional neural network along spatial dimensions for multiple long-term series forecasting. Appl Intell 54, 6606–6623 (2024). https://doi.org/10.1007/s10489-024-05526-7

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