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
Temporal Pattern Attention for Multivariate Time Series Forecasting
To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
edebrouwer/gru_ode_bayes
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NeurIPS 2019
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i. e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data.
Probabilistic Forecasting with Temporal Convolutional Neural Network
oneday88/deepTCN
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We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
Structured Inference Networks for Nonlinear State Space Models
We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
fanyun-sun/DARNN
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Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
Multivariate Time Series Forecasting with Transfer Entropy Graph
rrrussell/caugnn
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Multivariate time series (MTS) forecasting is an essential problem in many fields.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
nnzhan/MTGNN
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Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
