Multivariate Time Series Forecasting
95 papers with code • 8 benchmarks • 9 datasets
Libraries
Use these libraries to find Multivariate Time Series Forecasting models and implementationsDatasets
Most implemented papers
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals.
Long-term series forecasting with Query Selector -- efficient model of sparse attention
Various modifications of TRANSFORMER were recently used to solve time-series forecasting problem.
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
Multivariate time series forecasting focuses on predicting future values based on historical context.
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies.
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
However, the patterns of time series and the dependencies between them (i. e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data.
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
Utilizing DSW embedding and TSA layer, Crossformer establishes a Hierarchical Encoder-Decoder (HED) to use the information at different scales for the final forecasting.
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.