Bayesian Temporal Factorization for Multidimensional Time Series Prediction
By Lijun Sun, Xinyu Chen. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
302 citations recorded by the source corpus.
Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series—in particular spatiotemporal data—in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and model updating for real-time prediction and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and multi-step rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over existing state-of-the-art methods.
Subfields: Building and Construction, Computational Mathematics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/01f7b0cd89e75222b480f75a7fdcf7494ba9739c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
- Zhiyong Cui
- Ruimin Ke
- Ziyuan Pu
- Yinhai Wang
- Nizar Bouguila
- Saeed Rahmani
- Asiye Baghbani
- Zachary Patterson
In Computational Mathematics
In Electrical and Electronic Engineering
Citations in the corpus
- Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “A new Bayesian temporal matrix factorization method Sun and Chen (2019) is proposed to solve spatiotemporal data prediction problems when there is missing values in the input data.”