{"type":"ScholarlyArticle","title":"SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series","authors":[{"name":"Yi Dong","slug":"yi-dong","given":"Yi","family":"Dong","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"},{"name":"Liwen Zhang","slug":"liwen-zhang","given":"Liwen","family":"Zhang","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"},{"name":"Youcheng Zhang","slug":"youcheng-zhang","given":"Youcheng","family":"Zhang","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"},{"name":"Shi Peng","slug":"shi-peng","given":"Shi","family":"Peng","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"},{"name":"Wen Chen","slug":"wen-chen","given":"Wen","family":"Chen","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"},{"name":"Zhe Ma","slug":"zhe-ma","given":"Zhe","family":"Ma","affiliation":"Intelligent Science & Technology Academy of CASIC , Beijing , China"}],"abstract":"Representing temporal-structured samples is essential for effective time series analysis tasks. So far, recurrent networks, convolution networks and transformer-style models have been successively applied in temporal data representation, yielding notable results. However, most existing methods primarily focus on modeling and representing the variation patterns within time series in the time domain. As a highly abstracted information entity, 1D time series couples various patterns such as trends, seasonality, and dramatic changes (instantaneous high dynamic), it is difficult to exploit these highly coupled properties merely by analysis tools on purely time domain. To this end, we present Spectrogram Analysis and Representation Network (SpecAR-Net). SpecAR-Net aims at learning more comprehensive representations by modeling raw time series in both time and frequency domain, where an efficient joint extraction of time-frequency features is achieved through a group of learnable 2D multi-scale parallel complex convolution blocks. Experimental results show that the SpecAR-Net achieves excellent performance on 5 major downstream tasks i.e., classification, anomaly detection, imputation, long- and short-term forecasting. Code and appendix are available at https://github.com/Dongyi2go/SpecAR_Net.","doi":"10.24963/ijcai.2024/433","url":"https://ijcai.org/proceedings/2024/433","pdf":"https://ijcai.org/proceedings/2024/0433.pdf","markdown":"https://ijcai.org/proceedings/2024/433.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/433","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":433,"pages":{"first":3917,"last":3925},"keywords":["Machine Learning: ML: Representation learning","Machine Learning: ML: Convolutional networks"],"videos":[],"references":[{"position":1,"raw":"[Abdulaal et al., 2021] Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki. 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