Multivariate Time Series Forecasting
95 papers with code • 8 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Multivariate Time Series Forecasting
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Libraries
Use these libraries to find Multivariate Time Series Forecasting models and implementationsDatasets
MuJoCo
MIMIC-III
ETT
PhysioNet Challenge 2012
EarthNet2021
ExtMarker
Data Collected with Package Delivery Quadcopter Drone
Multivariate-Mobility-Paris
Beijing Traffic
Most implemented papers
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
microsoft/StemGNN
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NeurIPS 2020
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
zezhishao/step
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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
thinklab-sjtu/crossformer
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ICLR 2023
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
illidanlab/T-LSTM
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KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017
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
LeiBAI/STG2Seq
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Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
