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
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8, 068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets.
Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation
hank0626/llata
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Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting.
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
cfeng783/gtt
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We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting.
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
sjtu-quant/lift
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Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting.
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
yozhibo/msgnet
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To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution.
FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting
oncecwj/fcdnet
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Additionally, adopting a frequency-based perspective can effectively mitigate the influence of noise within MTS data, which helps capture more genuine dependencies.
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series.
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
liuxu77/unitime
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To address these issues, we propose UniTime for effective cross-domain time series learning.
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.
