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
Future-Guided Learning: A Predictive Approach To Enhance Time-Series Forecasting
Skye Gunasekaran, Assel Kembay, Hugo Ladret, Rui-Jie Zhu, Laurent Perrinet, Omid Kavehei, Jason Eshraghian
HiPPO-KAN: Efficient KAN Model for Time Series Analysis
SangJong Lee, Jin-Kwang Kim, JunHo Kim, TaeHan Kim, James Lee
FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, E. Alejandro Alanis, Ozlem Ozmen Garibay, Niloofar Yousefi
Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations
M. Germán-Morales, A.J. Rivera-Rivas, M.J. del Jesus Díaz, C.J. Carmona
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions
Abdullah Mamun, Krista S. Leonard, Megan E. Petrov, Matthew P. Buman, Hassan Ghasemzadeh
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, Frank Hutter