Time Series
Time series analysis focuses on understanding and modeling data points collected over time, aiming to extract patterns, make predictions, and gain insights from sequential information. Current research emphasizes developing advanced model architectures, such as transformers and recurrent neural networks (RNNs/LSTMs), to handle increasingly complex, high-dimensional, and non-stationary time series data, often incorporating techniques like attention mechanisms and mixture-of-experts models for improved efficiency and accuracy. This field is crucial for numerous applications across diverse domains, including finance, healthcare, and environmental monitoring, enabling better forecasting, anomaly detection, and decision-making based on temporal data.
Papers - Page 25
Toward a Foundation Model for Time Series Data
Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei ZhangComparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review
Ali Ebadi, Ebrahim SahafizadehTimeGPT-1
Azul Garza, Cristian Challu, Max Mergenthaler-Canseco
Enhanced LFTSformer: A Novel Long-Term Financial Time Series Prediction Model Using Advanced Feature Engineering and the DS Encoder Informer Architecture
Jianan Zhang, Hongyi DuanCausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan YuanMessage Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling
Shaoming Xu, Ankush Khandelwal, Arvind Renganathan, Vipin KumarMulti-Modal Financial Time-Series Retrieval Through Latent Space Projections
Tom Bamford, Andrea Coletta, Elizabeth Fons, Sriram Gopalakrishnan, Svitlana Vyetrenko, Tucker Balch, Manuela Veloso
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging
Nirhoshan Sivaroopan, Dumindu Bandara, Chamara Madarasingha, Guilluame Jourjon, Anura Jayasumana, Kanchana ThilakarathnaFinding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
Berken Utku Demirel, Christian HolzPredicting Temperature of Major Cities Using Machine Learning and Deep Learning
Wasiou Jaharabi, MD Ibrahim Al Hossain, Rownak Tahmid, Md. Zuhayer Islam, T. M. Saad Rayhan