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
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko
WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting
Quangao Liu, Ruiqi Li, Maowei Jiang, Wei Yang, Chen Liang, LongLong Pang, Zhuozhang Zou
Reinforced Decoder: Towards Training Recurrent Neural Networks for Time Series Forecasting
Qi Sima, Xinze Zhang, Yukun Bao, Siyue Yang, Liang Shen
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash
Fredformer: Frequency Debiased Transformer for Time Series Forecasting
Xihao Piao, Zheng Chen, Taichi Murayama, Yasuko Matsubara, Yasushi Sakurai
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Aditya Prakash
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng, Parisa Zehtabi, Sunandita Patra, Biplav Srivastava, Marco Valtorta
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
Difan Deng, Marius Lindauer
TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks
Ninghui Feng, Songning Lai, Fobao Zhou, Zhenxiao Yin, Hang Zhao
Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting
Sojung An, Tae-Jin Oh, Eunha Sohn, Donghyun Kim
TDT Loss Takes It All: Integrating Temporal Dependencies among Targets into Non-Autoregressive Time Series Forecasting
Qi Xiong, Kai Tang, Minbo Ma, Jie Xu, Tianrui Li