Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
@article{Jin2022MultivariateTS, title={Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs}, author={Ming Jin and Yu Zheng and Yuanhao Li and Siheng Chen and B. Yang and Shirui Pan}, journal={ArXiv}, year={2022}, volume={abs/2202.08408} }
—Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they have three fundamental limitations. (i). Discrete neural architectures : Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors. (ii). High…
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References
SHOWING 1-10 OF 38 REFERENCES
Discrete Graph Structure Learning for Forecasting Multiple Time Series
- Computer ScienceICLR
- 2021
This work proposes learning the structure simultaneously with the GNN if the graph is unknown, and casts the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution.
Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting
- Computer ScienceACM Trans. Knowl. Discov. Data
- 2021
The proposed HyDCNN is a novel hybrid framework based on fully Dilated CNN for time-series forecasting tasks, in which the proposed position-aware dilated CNNs are utilized to capture the sequential non-linear dynamics and an autoregressive model is leveraged to captured the sequential linear dependencies.
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
- Computer ScienceSIGIR
- 2018
A novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge of multivariate time series forecasting, using the Convolution Neural Network and the Recurrent Neural Network to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
- Computer Science, Environmental ScienceKDD
- 2021
This work proposes Spatial-Temporal Graph Ordinary Differential Equation Networks (STGODE), which captures spatial-temporal dynamics through a tensor-based ordinary differential equation (ODE), as a result, deeper networks can be constructed and spatial- Temporal features are utilized synchronously.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
- Computer ScienceICLR
- 2018
Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow and evaluates the framework on two real-world large scale road network traffic datasets and observes consistent improvement.
Forecasting energy time series with profile neural networks
- Computer Sciencee-Energy
- 2020
The proposed deep neural network architecture outperforms current state-of-the-art deep learning benchmark models regarding the forecasting accuracy on forecast horizons of one day and one week-ahead, improving the mean absolute scaled error by up to 25%, as well as regarding the trade-off between training time and accuracy.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
- Computer ScienceAAAI
- 2021
An efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment.
Graph Neural Controlled Differential Equations for Traffic Forecasting
- Computer ScienceAAAI
- 2022
The method of spatio-temporal graph neural controlled differential equation (STG-NCDE) shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
- Computer ScienceIJCAI
- 2018
This paper proposes a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain, and builds the model with complete convolutional structures, which enable much faster training speed with fewer parameters.