Time Series Forecasting
Time series forecasting aims to predict future values based on historical data, crucial for diverse applications from finance to healthcare. Current research emphasizes improving model accuracy and efficiency, focusing on transformer-based architectures, state-space models like Mamba, and hybrid approaches combining their strengths, as well as exploring data augmentation and explainable AI techniques. These advancements are driving improvements in forecasting accuracy and interpretability, leading to better decision-making across various sectors and contributing to a deeper understanding of complex temporal dynamics.
Papers
In-context Time Series Predictor
Jiecheng Lu, Yan Sun, Shihao Yang
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
Qingxiang Liu, Xu Liu, Chenghao Liu, Qingsong Wen, Yuxuan Liang
Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues
Zhijian Xu, Yuxuan Bian, Jianyuan Zhong, Xiangyu Wen, Qiang Xu
NFCL: Simply interpretable neural networks for a short-term multivariate forecasting
Wonkeun Jo, Dongil Kim
Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System
Abdullah M. Zyarah, Dhireesha Kudithipudi
FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting
Ruiqi Li, Maowei Jiang, Kai Wang, Kaiduo Feng, Quangao Liu, Yue Sun, Xiufang Zhou
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei, Yongjun Xu
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
José Leites, Vitor Cerqueira, Carlos Soares