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
Latest papers with no code
Kernel-U-Net: Symmetric and Hierarchical Architecture for Multivariate Time Series Forecasting
Time series forecasting task predicts future trends based on historical information.
Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecasting
These extra routes offer a more robust and refined regression to the data, particularly when the amount of observation is limited and the capacity of simple linear layers to capture dependencies declines.
Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting
In recent developments, predictive models for multivariate time series analysis have exhibited commendable performance through the adoption of the prevalent principle of channel independence.
Dozerformer: Sequence Adaptive Sparse Transformer for Multivariate Time Series Forecasting
(3) Vary, allows queries to selectively attend to keys from a subset of the historical sequence.
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
Multivariate time series is prevalent in many scientific and industrial domains.
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
Pyramidal Hidden Markov Model For Multivariate Time Series Forecasting
The Hidden Markov Model (HMM) can predict the future value of a time series based on its current and previous values, making it a powerful algorithm for handling various types of time series.
Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting
To address these issues, we propose Spatial-Temporal Masked Autoencoders (STMAE), an MTS forecasting framework that leverages masked autoencoders to enhance the performance of spatial-temporal baseline models.
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records.
