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Time series forecasting has attracted significant attention in recent decades.
Stl: A seasonal-trend decomposition
Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning · 1990
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Wavelet methods for time series analysis
Donald B Percival and Andrew T Walden · 2000
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Time series forecasting using a hybrid arima and neural network model
G Peter Zhang · 2003
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Compactly supported radial basis function kernels
Hao Helen Zhang, Mark G Genton, and Peng Liu · 2004
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On the marriage of lp-norms and edit distance
Lei Chen and Raymond Ng · 2004
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Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
Djamel Benaouda, Fionn Murtagh, J-L Starck, and Olivier Renaud · 2006
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Querying and mining of time series data: experimental comparison of representations and distance measures
Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, and Eamonn Keogh · 2008
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Energy time series forecasting based on pattern sequence similarity
Francisco Martinez Alvarez, Alicia Troncoso, Jose C Riquelme, and Jesus S Aguilar Ruiz · 2010
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An empirical comparison of machine learning models for time series forecasting
Nesreen K Ahmed, Amir F Atiya, Neamat El Gayar, and Hisham El-Shishiny · 2010
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A fuzzy self-constructing feature clustering algorithm for text classification
Jung-Yi Jiang, Ren-Jia Liou, and Shie-Jue Lee · 2010
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Unsupervised video anomaly detection using feature clustering
Hao Li, Alin Achim, and D Bull · 2012
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Unsupervised clustering approach for network anomaly detection
Iwan Syarif, Adam Prugel-Bennett, and Gary Wills · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Trend modeling for traffic time series analysis: An integrated study
Li Li, Xiaonan Su, Yi Zhang, Yuetong Lin, and Zhiheng Li · 2015
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ElectricityLoadDiagrams20112014
Artur Trindade · 2015
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Forecasting traffic time series with multivariate predicting method
Yi Yin and Pengjian Shang · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Clapp: A self constructing feature clustering approach for anomaly detection
Rajesh Kumar Gunupudi, Mangathayaru Nimmala, Narsimha Gugulothu, and Suresh Reddy Gali · 2017
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Modeling long-and short-term temporal patterns with deep neural networks. corr abs/1703.07015 (2017)
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2017
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Convolutional neural networks for energy time series forecasting
Irena Koprinska, Dengsong Wu, and Zheng Wang · 2018
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Forecasting at scale
Sean J Taylor and Benjamin Letham · 2018
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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The m4 competition: Results, findings, conclusion and way forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
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Dynamic time warping and geometric edit distance: Breaking the quadratic barrier
Omer Gold and Micha Sharir · 2018
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M4 competitor’s guide: prizes and rules
M4 Team et al · 2018
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Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting
Renzhuo Wan, Shuping Mei, Jun Wang, Min Liu, and Fan Yang · 2019
Cited alongside, same era.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S Dhillon · 2019
Cited alongside, same era.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Multivariate time series dataset for space weather data analytics
Rafal A Angryk, Petrus C Martens, Berkay Aydin, Dustin Kempton, Sushant S Mahajan, Sunitha Basodi, Azim Ahmadzadeh, Xumin Cai, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, et al · 2020
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2022
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Non-stationary transformers: Rethinking the stationarity in time series forecasting
Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2022
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2022
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Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures
Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, and Jian Li · 2022
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Micn: Multi-scale local and global context modeling for long-term series forecasting
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Cited alongside, same era.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl · 2020
Cited alongside, same era.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
Cited alongside, same era.
Fast robuststl: Efficient and robust seasonal-trend decomposition for time series with complex patterns
Qingsong Wen, Zhe Zhang, Yan Li, and Liang Sun · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Cited alongside, same era.
A review and comparison of time series similarity measures
Maša Kljun and M Tersˇek · 2020
Cited alongside, same era.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
Cited alongside, same era.
Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao · 2022
Later among the works it cites.
Film: Frequency improved legendre memory model for long-term time series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, et al · 2022
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Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, et al · 2023
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Tsmixer: An all-mlp architecture for time series forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister · 2023
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Revisiting long-term time series forecasting: An investigation on linear mapping
Zhe Li, Shiyi Qi, Yiduo Li, and Zenglin Xu · 2023
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Is channel independent strategy optimal for time series forecasting?
Yuan Peiwen and Zhu Changsheng · 2023
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Lu Han, Han-Jia Ye, and De-Chuan Zhan · 2023
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Forecasting the dynamic correlation of stock indices based on deep learning method
Jian Ni and Yue Xu · 2023
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Spatial correlation in weather forecast accuracy: a functional time series approach
Phillip A Jang and David S Matteson · 2023
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itransformer: Inverted transformers are effective for time series forecasting
Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long · 2023
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Long-term forecasting with tide: Time-series dense encoder
Abhimanyu Das, Weihao Kong, Andrew Leach, Rajat Sen, and Rose Yu · 2023
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Frequency-domain mlps are more effective learners in time series forecasting
Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, and Zhendong Niu · 2023
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Card: Channel aligned robust blend transformer for time series forecasting
Xue Wang, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, and Rong Jin · 2023
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Token pooling in vision transformers for image classification
Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, and Oncel Tuzel · 2023
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An integrated clustering and bert framework for improved topic modeling
Lijimol George and P Sumathy · 2023
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Spatio-temporal self-supervised learning for traffic flow prediction
Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, and Yu Zheng · 2023
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Self-supervised spatiotemporal masking strategy-based models for traffic flow forecasting
Gang Liu, Silu He, Xing Han, Qinyao Luo, Ronghua Du, Xinsha Fu, and Ling Zhao · 2023
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Mts-mixers: Multivariate time series forecasting via factorized temporal and channel mixing
Zhe Li, Zhongwen Rao, Lujia Pan, and Zenglin Xu · 2023
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Adaptive normalization for non-stationary time series forecasting: A temporal slice perspective
Zhiding Liu, Mingyue Cheng, Zhi Li, Zhenya Huang, Qi Liu, Yanhu Xie, and Enhong Chen · 2023
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Time-llm: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al · 2023
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Mlinear: Rethink the linear model for time-series forecasting
Wei Li, Xiangxu Meng, Chuhao Chen, and Jianing Chen · 2023
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Efficient high-resolution time series classification via attention kronecker decomposition
Aosong Feng, Jialin Chen, Juan Garza, Brooklyn Berry, Francisco Salazar, Yifeng Gao, Rex Ying, and Leandros Tassiulas · 2024
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Multi-scale transformer pyramid networks for multivariate time series forecasting
Yifan Zhang, Rui Wu, Sergiu M Dascalu, and Frederick C Harris · 2024
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