Vector Autoregressive Models for Multivariate Time Series

558 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “This leads to a range of Markov-like models with stochastic driving terms, such as the vector autoregressive model (VAR) [4] or the”
Long-term Forecasting with TiDE: Time-series Dense Encoder (2023, unknown): “This includes the classical VAR models (Zivot and Wang, 2006).”
A decoder-only foundation model for time-series forecasting (2023, methodology): “(iii) Global multi-variate models that take in the past of all time-series in the dataset to predict the future of all the time-series.”
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019, methodology): “The statistical models used for trafﬁc prediction include HA, ARIMA (Williams and Hoel 2003), VAR (Zivot and Wang 2006), etc.”
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, methodology): “The most representative algorithms are Historical Average (HA), Auto-Regressive Integrated Moving Average (ARIMA) [11], and Vector Auto-Regressive (VAR) [12].”
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, background): “VAR [58]: Vector Auto-Regression is an advanced time series model, which captures the pairwise relationships among multiple time series.”