Correlated Time Series

Correlated time series analysis focuses on understanding and predicting the relationships between multiple time-dependent datasets. Current research emphasizes developing efficient and interpretable models, including convolutional neural networks, recurrent neural networks (LSTMs and BiLSTMs), and ensemble methods like Random Forests, to improve forecasting accuracy and understand underlying patterns. These advancements are driven by the need for effective solutions in diverse applications such as traffic prediction, climate modeling, and structural health monitoring, where accurate forecasting and insightful pattern recognition are crucial. The field is also exploring automated model design to optimize performance and reduce the reliance on manual architecture selection.

Papers

September 2, 2024

Correlating Time Series with Interpretable Convolutional Kernels
Xinyu Chen, HanQin Cai, Fuqiang Liu, Jinhua Zhao
Time Series Convolutional Kernel Tensor Factorization Correlated Time Series

February 23, 2023

LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Zhichen Lai, Dalin Zhang, Huan Li, Christian S. Jensen, Hua Lu, Yan Zhao
Correlated Time Series Correlated Time Series Forecasting

January 27, 2023

Machine Learning Approach and Extreme Value Theory to Correlated Stochastic Time Series with Application to Tree Ring Data
Omar Alzeley, Sadiah Aljeddani
Machine Learning Application Proficiency Random Forest Machine Learning Approach Climate Model Extreme Value Theory Tree Ring Correlated Time Series

September 12, 2022

A topological analysis of cointegrated data: a Z24 Bridge case study
Tristan Gowdridge, Elizabeth Cross, Nikolaos Dervilis, Keith Worden
Autoregressive Model Topological Data Analysis Topological Feature Topological Analysis Correlated Time Series

April 21, 2022

A data filling methodology for time series based on CNN and (Bi)LSTM neural networks
Kostas Tzoumpas, Aaron Estrada, Pietro Miraglio, Pietro Zambelli
Neural Network Raw Data Time Series Long Short Term Memory CNN Network Bi LSTM Temperature Evolution Correlated Time Series

March 31, 2022

Learning from many trajectories
Stephen Tu, Roy Frostig, Mahdi Soltanolkotabi
Handwritten Trajectory Efficient Learning Statistical Learning Theory Many Trajectory Correlated Time Series

March 30, 2022

Theory of Acceleration of Decision Making by Correlated Time Sequences
Norihiro Okada, Tomoki Yamagami, Nicolas Chauvet, Yusuke Ito, Mikio Hasegawa, Makoto Naruse
Theoretical Understanding Decision Making Low Rate ACCELERATION Photonic Accelerator Correlated Time Series

December 21, 2021

AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen
Extended Version Univariate Time Series Forecasting Correlated Time Series Correlated Time Series Forecasting