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
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning
manjimnav/TSSLayer
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International Work-Conference on Artificial Neural Networks 2023
However, selecting the right features can be challenging for time series models.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
deepkashiwa20/Urban_Concept_Drift
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Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features.
SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
To bridge this gap, this paper introduces a novel series-aware framework, explicitly designed to emphasize the significance of such dependencies.
PrimeNet: Pre-Training for Irregular Multivariate Time Series
ranakroychowdhury/PrimeNet
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AAAI Conference on Artificial Intelligence 2023
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%.
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
zyj-111/gcformer
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On the other hand, the long input sequence usually leads to large model size and high time complexity.
A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT).
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
JLDeng/SCNN
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The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Forecasting Irregularly Sampled Time Series using Graphs
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences.
