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
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features.
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones.
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay.
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations.
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Attention as Robust Representation for Time Series Forecasting
Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.
Kernel-U-Net: Symmetric and Hierarchical Architecture for Multivariate Time Series Forecasting
Time series forecasting task predicts future trends based on historical information.
