{"type":"ScholarlyArticle","title":"Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning","authors":[{"name":"Jihyeon Seong","slug":"jihyeon-seong","given":"Jihyeon","family":"Seong","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea"},{"name":"Sekwang Oh","slug":"sekwang-oh","given":"Sekwang","family":"Oh","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea"},{"name":"Jaesik Choi","slug":"jaesik-choi","given":"Jaesik","family":"Choi","affiliation":"Korea Advanced Institute of Science and Technology (KAIST) , South Korea; INEEJI , South Korea"}],"abstract":"Trend filtering simplifies complex time series data by applying smoothness to filter out noise while emphasizing proximity to the original data. However, existing trend filtering methods fail to reflect abrupt changes in the trend due to `approximateness,' resulting in constant smoothness. This approximateness uniformly filters out the tail distribution of time series data, characterized by extreme values, including both abrupt changes and noise. In this paper, we propose Trend Point Detection formulated as a Markov Decision Process (MDP), a novel approach to identifying essential points that should be reflected in the trend, departing from approximations. We term these essential points as Dynamic Trend Points (DTPs) and extract trends by interpolating them. To identify DTPs, we utilize Reinforcement Learning (RL) within a discrete action space and a forecasting sum-of-squares loss function as a reward, referred to as the Dynamic Trend Filtering network (DTF-net). DTF-net integrates flexible noise filtering, preserving critical original subsequences while removing noise as required for other subsequences. We demonstrate that DTF-net excels at capturing abrupt changes compared to other trend filtering algorithms and enhances forecasting performance, as abrupt changes are predicted rather than smoothed out.","doi":"10.24963/ijcai.2024/257","url":"https://ijcai.org/proceedings/2024/257","pdf":"https://ijcai.org/proceedings/2024/0257.pdf","markdown":"https://ijcai.org/proceedings/2024/257.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/257","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":"Data Mining","track":"Main Track","number":257,"pages":{"first":2324,"last":2332},"keywords":["Machine Learning: ML: Reinforcement learning","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Adams and MacKay, 2007] Ryan Prescott Adams and David JC MacKay. Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742, 2007.","title":"Bayesian online changepoint detection","authors":"Ryan Prescott, Adams, David Jc Mackay","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Altmann and Kantz, 2005] Eduardo G Altmann and Holger Kantz. Recurrence time analysis, long-term correlations, and extreme events. Physical Review E, 71(5):056106, 2005.","title":"Recurrence time analysis, long-term correlations, and extreme events","authors":"G Eduardo, Holger Altmann, Kantz","year":"2005","venue":"Physical Review E","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ansari et al., 2021] Abdul Fatir Ansari, Konstantinos Beni- dis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J Smola, Bernie Wang, and Tim Januschowski. Deep explicit duration switching models for time series. In Proceedings of Advances in Neural Information Process- ing Systems (NeurIPS'21), 2021.","title":"Deep explicit duration switching models for time series","authors":"Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J Smola, Bernie Wang, Tim Januschowski","year":"2021","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS'21)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Banerjee and Gelfand, 2003] Sudipto Banerjee and AE Gelfand. On smoothness properties of spatial processes. Journal of Multivariate Analysis, 84(1):85- 100, 2003.","title":"On smoothness properties of spatial processes","authors":"Sudipto Banerjee, Gelfand","year":"2003","venue":"Journal of Multivariate Analysis","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Banerjee et al., 2005] Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, Joydeep Ghosh, and John Lafferty. Clustering with bregman divergences. Journal of Machine Learning Research, 6(10), 2005.","title":"Clustering with bregman divergences","authors":"Arindam Banerjee, Srujana Merugu, Joydeep Inderjit S Dhillon, John Ghosh, Lafferty","year":"2005","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Biyik et al., 2020] Erdem Biyik, Nicolas Huynh, Mykel J. Kochenderfer, and Dorsa Sadigh. Active preference-based gaussian process regression for reward learning. In Pro- ceedings of Robotics: Science and Systems (RSS'20), 2020.","title":"Active preference-based gaussian process regression for reward learning","authors":"Erdem Biyik, Nicolas Huynh, J Mykel, Dorsa Kochenderfer, Sadigh","year":"2020","venue":"Proceedings of Robotics: Science and Systems (RSS'20","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Caldarelli et al., 2022] Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, and Andreas Krause. Adaptive Gaussian process change point detection. In Proceedings of Interna- tional Conference on Machine Learning (ICML'22), 2022.","title":"Adaptive Gaussian process change point detection","authors":"Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause","year":"2022","venue":"Proceedings of International Conference on Machine Learning (ICML'22)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chan et al., 2001] Tony F Chan, Stanley Osher, and Jian- hong Shen. The digital tv filter and nonlinear denoising. IEEE Transactions on Image processing, 10(2):231-241, 2001.","title":"The digital tv filter and nonlinear denoising","authors":"Stanley Tony F Chan, Jianhong Osher, Shen","year":"2001","venue":"IEEE Transactions on Image processing","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chow, 1960] Gregory C Chow. Tests of equality between sets of coefficients in two linear regressions. Economet- rica: Journal of the Econometric Society, pages 591-605, 1960.","title":"Tests of equality between sets of coefficients in two linear regressions","authors":"C Gregory, Chow","year":"1960","venue":"Econometrica: Journal of the Econometric Society","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Craigmile and Percival, 2002] Peter F Craigmile and Don- ald B Percival. Wavelet-based trend detection and estima- tion. Entry in the Encyclopedia of Environmetrics. Chich- ester, UK: John Wiley & Sons, pages 2334-2338, 2002.","title":"Wavelet-based trend detection and estimation","authors":"F Peter, Donald B Craigmile, Percival","year":"2002","venue":"Entry in the Encyclopedia of Environmetrics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dickey and Fuller, 1979] David A Dickey and Wayne A Fuller. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Sta- tistical Association, 74(366a):427-431, 1979.","title":"Distribution of the estimators for autoregressive time series with a unit root","authors":"Dickey, Fuller ; David, A Dickey, Wayne A Fuller","year":"1979","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ding et al., 2019] Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, and Xiangnan He. Modeling extreme events in time series prediction. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19), 2019.","title":"Modeling extreme events in time series prediction","authors":"Ding","year":"2019","venue":"Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fisher and Tippett, 1928] Ronald Aylmer Fisher and Leonard Henry Caleb Tippett. Limiting forms of the fre- quency distribution of the largest or smallest member of a sample. In Mathematical Proceedings of the Cambridge Philosophical Society, 1928.","title":"Limiting forms of the frequency distribution of the largest or smallest member of a sample","authors":"Tippett ; Fisher, Aylmer Ronald, Leonard Fisher, Caleb Henry, Tippett","year":"1928","venue":"Mathematical Proceedings of the Cambridge Philosophical Society","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Geyer, 1992] Charles J. Geyer. Practical Markov Chain Monte Carlo. Statistical Science, 7(4):473 -483, 1992.","title":"Practical Markov Chain Monte Carlo","authors":"Charles J Geyer","year":"1992","venue":"Statistical Science","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Gong and Huang, 2012] Rongsheng Gong and Samuel H. Huang. A kolmogorov-smirnov statistic based segmenta- tion approach to learning from imbalanced datasets: With application in property refinance prediction. Expert Syst. Appl., 39(6):6192-6200, may 2012.","title":"A kolmogorov-smirnov statistic based segmentation approach to learning from imbalanced datasets: With application in property refinance prediction","authors":"Huang Gong, Rongsheng Gong, Samuel H Huang","year":"2012","venue":"Expert Syst. Appl","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hallac et al., 2017] David Hallac, Sagar Vare, Stephen Boyd, and Jure Leskovec. Toeplitz inverse covariance- based clustering of multivariate time series data. In Pro- ceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17), 2017.","title":"Toeplitz inverse covariancebased clustering of multivariate time series data","authors":"David Hallac, Sagar Vare, Stephen Boyd, Jure Leskovec","year":"2017","venue":"Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Han et al., 2019] Jiyeon Han, Kyowoon Lee, Anh Tong, and Jaesik Choi. Confirmatory bayesian online change point detection in the covariance structure of gaussian processes. In Proceedings of International Joint Conference on Arti- ficial Intelligence (IJCAI'19), 2019.","title":"Confirmatory bayesian online change point detection in the covariance structure of gaussian processes","authors":"Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi","year":"2019","venue":"Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'19)","doi":null,"url":null,"internal_url":"/proceedings/2019/340"},{"position":18,"raw":"[Hodrick and Prescott, 1997] Robert J Hodrick and Ed- ward C Prescott. Postwar us business cycles: an empir- ical investigation. Journal of Money, credit, and Banking, pages 1-16, 1997.","title":"Postwar us business cycles: an empirical investigation","authors":"J Robert, Edward C Hodrick, Prescott","year":"1997","venue":"Journal of Money, credit, and Banking","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kim et al., 2009] Seung-Jean Kim, Kwangmoo Koh, Stephen Boyd, and Dimitry Gorinevsky. \\ell 1 trend filtering. SIAM review, 51(2):339-360, 2009.","title":"1 trend filtering","authors":"Seung-Jean Kim, Kwangmoo Koh, Stephen Boyd, Dimitry Gorinevsky","year":"2009","venue":"SIAM review","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kuss and Rasmussen, 2003] Malte Kuss and Carl Ras- mussen. Gaussian processes in reinforcement learning. In Proceedings of Advances in Neural Information Process- ing Systems (NeurIPS'03), 2003.","title":"Gaussian processes in reinforcement learning","authors":"Rasmussen Kuss","year":"2003","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS'03)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Leser, 1961] Conrad Emanuel Victor Leser. A simple method of trend construction. Journal of the Royal Sta- tistical Society: Series B (Methodological), 23(1):91-107, 1961.","title":"A simple method of trend construction","authors":"Conrad Emanuel, Victor Leser","year":"1961","venue":"Journal of the Royal Statistical Society: Series B (Methodological)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2022a] Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, and Jian Guo. Finrl-meta: Market environments and benchmarks for data-driven financial re- inforcement learning. In Proceedings of Advances in Neu- ral Information Processing Systems (NeurIPS'22), 2022.","title":"Finrl-meta: Market environments and benchmarks for data-driven financial reinforcement learning","authors":"Liu","year":"2022","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS'22)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2022b] Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. Non-stationary transformers: Explor- ing the stationarity in time series forecasting. In Proceed- ings of Advances in Neural Information Processing Sys- tems (NeurIPS'22), 2022.","title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","authors":"Liu","year":"2022","venue":"Proceed-ings of Advances in Neural Information Processing Systems (NeurIPS'22)","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lorenz, 1963] Edward N Lorenz. Deterministic nonperi- odic flow. Journal of Atmospheric Sciences, 20(2):130- 141, 1963.","title":"Deterministic nonperiodic flow","authors":"N Edward, Lorenz","year":"1963","venue":"Journal of Atmospheric Sciences","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Belle- mare, Alex Graves, Martin Riedmiller, Andreas K Fidje- land, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.","title":"Human-level control through deep reinforcement learning","authors":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mnih et al., 2016] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asyn- chronous methods for deep reinforcement learning. In Proceedings of International Conference on Machine Learning (ICML'16), 2016.","title":"Asynchronous methods for deep reinforcement learning","authors":"Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu","year":"2016","venue":"Proceedings of International Conference on Machine Learning (ICML'16)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Nie et al., 2023] Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. A time series is worth 64 words: Long-term forecasting with transformers. In Proceedings of International Conference on Learning Representations (ICLR'23), 2023.","title":"A time series is worth 64 words: Long-term forecasting with transformers","authors":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, Jayant Kalagnanam","year":"2023","venue":"Proceedings of International Conference on Learning Representations (ICLR'23)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Park et al., 2020] Youngjin Park, Deokjun Eom, Byoungki Seo, and Jaesik Choi. Improved predictive deep tempo- ral neural networks with trend filtering. In Proceedings of ACM International Conference on AI in Finance (ICAIF '20), 2020.","title":"Improved predictive deep temporal neural networks with trend filtering","authors":"Youngjin Park, Deokjun Eom, Byoungki Seo, Jaesik Choi","year":"2020","venue":"Proceedings of ACM International Conference on AI in Finance (ICAIF '20)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ploberger and Krämer, 1992] Werner Ploberger and Walter Krämer. The cusum test with ols residuals. Econometrica: Journal of the Econometric Society, pages 271-285, 1992.","title":"The cusum test with ols residuals","authors":"Krämer Ploberger, Werner Ploberger, Walter Krämer","year":"1992","venue":"Econometrica: Journal of the Econometric Society","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Raj et al., 2016] Vidwath Raj, Sven Magg, and Stefan Wermter. Towards effective classification of imbalanced data with convolutional neural networks. Artificial Neural Networks in Pattern Recognition, pages 150-162, 2016.","title":"Towards effective classification of imbalanced data with convolutional neural networks","authors":"Raj","year":"2016","venue":"Artificial Neural Networks in Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rosenblatt, 1956] Murray Rosenblatt. Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics, pages 832-837, 1956.","title":"Remarks on some nonparametric estimates of a density function","authors":"Murray Rosenblatt","year":"1956","venue":"The Annals of Mathematical Statistics","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Schulman et al., 2017] John Schulman, Filip Wolski, Pra- fulla Dhariwal, Alec Radford, and Oleg Klimov. Prox- imal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.","title":"Proximal policy optimization algorithms","authors":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Siegel, 1982] Andrew F Siegel. Robust regression using re- peated medians. Biometrika, 69(1):242-244, 1982.","title":"Robust regression using repeated medians","authors":"F Andrew, Siegel","year":"1982","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Sutton and Barto, 2018] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.","title":"Reinforcement learning: An introduction","authors":"S Richard, Andrew G Sutton, Barto","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Van den Berg et al., 2008] Jeroen Van den Berg, Bertrand Candelon, and Jean-Pierre Urbain. A cautious note on the use of panel models to predict financial crises. Economics Letters, 101(1):80-83, 2008.","title":"A cautious note on the use of panel models to predict financial crises","authors":"Van Den Berg","year":"2008","venue":"Economics Letters","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[von Bortkiewicz, 1921] Ladislaus von Bortkiewicz. Varia- tionsbreite und mittlerer Fehler. Berliner Mathematische Gesellschaft, 1921.","title":"Ladislaus von Bortkiewicz. Variationsbreite und mittlerer Fehler","authors":"Von Bortkiewicz","year":"1921","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wen et al., 2019] Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, and Jian Tan. Robusttrend: A huber loss with a combined first and second order difference reg- ularization for time series trend filtering. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'19), 2019.","title":"Robusttrend: A huber loss with a combined first and second order difference regularization for time series trend filtering","authors":"Wen","year":"2019","venue":"Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'19)","doi":null,"url":null,"internal_url":"/proceedings/2019/535"},{"position":38,"raw":"[Williams and Rasmussen, 1995] Christopher Williams and Carl Rasmussen. Gaussian processes for regression. In Proceedings of Advances in Neural Information Process- ing Systems (NeurIPS'95), 1995.","title":"Gaussian processes for regression","authors":"Christopher Williams, Carl Rasmussen","year":"1995","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS'95)","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu and Ortiz, 2021] Tong Wu and Jorge Ortiz. Rlad: Time series anomaly detection through reinforcement learning and active learning. ArXiv, abs/2104.00543, 2021.","title":"Rlad: Time series anomaly detection through reinforcement learning and active learning","authors":"Tong Wu, Jorge Ortiz","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wu et al., 2007] Zhaohua Wu, Norden E Huang, Steven R Long, and Chung-Kang Peng. On the trend, detrend- ing, and variability of nonlinear and nonstationary time series. National Academy of Sciences, 104(38):14889- 14894, 2007.","title":"On the trend, detrending, and variability of nonlinear and nonstationary time series","authors":"Zhaohua Wu, Norden E Huang, Steven R Long, Chung-Kang Peng","year":"2007","venue":"National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wu et al., 2021] Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecasting. In Proceedings of Advances in Neural Information Pro- cessing Systems (NeurIPS'21), 2021.","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","authors":"Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long","year":"2021","venue":"Proceedings of Advances in Neural Information Processing Systems (NeurIPS'21)","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wu et al., 2023] Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Temporal 2d-variation modeling for general time series analysis. In Proceedings of International Conference on Learning Representations (ICLR'23), 2023.","title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","authors":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long","year":"2023","venue":"Proceedings of International Conference on Learning Representations (ICLR'23)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2022] Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long. Anomaly transformer: Time series anomaly detection with association discrepancy. In Pro- ceedings of International Conference on Learning Repre- sentations (ICLR'22), 2022.","title":"Anomaly transformer: Time series anomaly detection with association discrepancy","authors":"Xu","year":"2022","venue":"Proceedings of International Conference on Learning Representations (ICLR'22)","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yang et al., 2023] Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun. Dcdetector: Dual attention contrastive representation learning for time series anomaly detection. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023.","title":"Dcdetector: Dual attention contrastive representation learning for time series anomaly detection","authors":"Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun","year":"2023","venue":"Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23)","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zeng et al., 2023] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series fore- casting? In Proceedings of the AAAI Conference on Arti- ficial Intelligence (AAAI'23), 2023.","title":"Are transformers effective for time series forecasting?","authors":"Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'23)","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhou et al., 2021] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wan- cai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20), 2021.","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","authors":"Zhou","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20)","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhou et al., 2022] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. Fedformer: Fre- quency enhanced decomposed transformer for long-term series forecasting. In Proceedings of International Con- ference on Machine Learning (ICML'22), 2022.","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","authors":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin","year":"2022","venue":"Proceedings of International Conference on Machine Learning (ICML'22)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}