Multivariate Time Series Forecasting
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Benchmarks
USHCN-Daily
MuJoCo
MIMIC-III
PhysioNet Challenge 2012
ETTh1 (192) Multivariate
ETTh1 (96) Multivariate
BPI challenge '12
ETTh1 (336) Multivariate
Helpdesk
Electricity
Traffic
AEP
ETTh1 (48) Multivariate
ETTh1 (720) Multivariate
ETTh2 (720) Multivariate
ExtMarker
Weather
Most implemented
Neural Ordinary Differential Equations
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Latent ODEs for Irregularly-Sampled Time Series
Papers
Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting
In non-stationary multivariate time series, different variables and samples often exhibit heterogeneous latent dynamic states, while existing deep forecasting models usually compress them into a unified end-to-end mappin…
Multivariate Time Series ForecastingCLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generative models have shown strong results on …
Probabilistic Time Series ForecastingMultivariate Time Series ForecastingRethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioni…
Multivariate Time Series ForecastingAsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, where…
Multivariate Time Series ForecastingMultivariate Time Series Forecasting needs Cross Variable Loss
Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-variable dependencies in historical obse…
Multivariate Time Series ForecastingMulti-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series
The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns an…
Multivariate Time Series Forecasting