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Found 17 papers, 12 papers with code
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
Experimentally, TimeXer significantly improves time series forecasting with exogenous variables and achieves consistent state-of-the-art performance in twelve real-world forecasting benchmarks.
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
To emphasize temporal correlation modeling, this paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Transformers have empowered many milestones across various fields and have recently been applied to solve partial differential equations (PDEs).
EuLagNet: Eulerian Fluid Prediction with Lagrangian Dynamics
Accurately predicting the future fluid is important to extensive areas, such as meteorology, oceanology and aerodynamics.
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Compared with previous velocity estimating methods, HelmFluid is faithfully derived from Helmholtz theorem and ravels out complex fluid dynamics with physically interpretable evidence.
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series.
Solving High-Dimensional PDEs with Latent Spectral Models
A burgeoning paradigm is learning neural operators to approximate the input-output mappings of PDEs.
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time.
Supported Policy Optimization for Offline Reinforcement Learning
Policy constraint methods to offline reinforcement learning (RL) typically utilize parameterization or regularization that constrains the policy to perform actions within the support set of the behavior policy.
Flowformer: Linearizing Transformers with Conservation Flows
By respectively conserving the incoming flow of sinks for source competition and the outgoing flow of sources for sink allocation, Flow-Attention inherently generates informative attentions without using specific inductive biases.
Ranked #4 on
D4RL
on D4RL
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
To this end, we propose ModeRNN, which introduces a novel method to learn structured hidden representations between recurrent states.
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion.
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism.
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction.
