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
Training and Evaluating Causal Forecasting Models for Time-Series
Thomas Crasson, Yacine Nabet, Mathias Lécuyer
In-Context Fine-Tuning for Time-Series Foundation Models
Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
Zongjiang Shang, Ling Chen, Binqing wu, Dongliang Cui
FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities
Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk
Higher-order Cross-structural Embedding Model for Time Series Analysis
Guancen Lin, Cong Shen, Aijing Lin
Community search signatures as foundation features for human-centered geospatial modeling
Mimi Sun, Chaitanya Kamath, Mohit Agarwal, Arbaaz Muslim, Hector Yee, David Schottlander, Shailesh Bavadekar, Niv Efron, Shravya Shetty, Gautam Prasad
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
Can Chen, Gabriel Oliveira, Hossein Sharifi Noghabi, Tristan Sylvain
TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
Shiyu Wang, Jiawei Li, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin