SOTAVerified

Time Series Forecasting

Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).

( Image credit: ThaiBinh Nguyen )

Papers

Showing 201–250 of 1609 papers

TitleStatusHype
WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning ModelsCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
Disentangled Interpretable Representation for Efficient Long-term Time Series ForecastingCode1
Peri-midFormer: Periodic Pyramid Transformer for Time Series AnalysisCode1
FlexTSF: A Universal Forecasting Model for Time Series with Variable RegularitiesCode1
WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series ForecastingCode1
FACTS: A Factored State-Space Framework For World ModellingCode1
Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series ForecastingCode1
Large Language Models for Financial Aid in Financial Time-series ForecastingCode1
LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series ForecastingCode1
LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for the Long-term Time Series ForecastingCode1
LLM-Mixer: Multiscale Mixing in LLMs for Time Series ForecastingCode1
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space ModelsCode1
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series ForecastingCode1
Can LLMs Understand Time Series Anomalies?Code1
Autoregressive Moving-average Attention Mechanism for Time Series ForecastingCode1
BACKTIME: Backdoor Attacks on Multivariate Time Series ForecastingCode1
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution ShiftsCode1
Practical Forecasting of Cryptocoins Timeseries using Correlation PatternsCode1
Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You NeedCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series ForecastingCode1
Risk and cross validation in ridge regression with correlated samplesCode1
Scalable Transformer for High Dimensional Multivariate Time Series ForecastingCode1
Channel-Aware Low-Rank Adaptation in Time Series ForecastingCode1
Revisiting Attention for Multivariate Time Series ForecastingCode1
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative RegularizationCode1
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time SeriesCode1
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical ImagesCode1
SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series ForecastingCode1
XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptationCode1
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant LearningCode1
DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series ForecastingCode1
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series ForecastingCode1
TimeSieve: Extracting Temporal Dynamics through Information BottlenecksCode1
CondTSF: One-line Plugin of Dataset Condensation for Time Series ForecastingCode1
Hierarchical Classification Auxiliary Network for Time Series ForecastingCode1
Scaling Law for Time Series ForecastingCode1
Interpretable Multivariate Time Series Forecasting Using Neural Fourier TransformCode1
Attention as an RNNCode1
FAITH: Frequency-domain Attention In Two Horizons for Time Series ForecastingCode1
Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla TransformersCode1
VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series ForecastingCode1
LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series ForecastingCode1
Forecasting with Hyper-TreesCode1
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series ForecastingCode1
An Analysis of Linear Time Series Forecasting ModelsCode1
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous VariablesCode1
Generative Pretrained Hierarchical Transformer for Time Series ForecastingCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InformerMSE0.88—Unverified
2QuerySelectorMSE0.85—Unverified
3TransformerMSE0.83—Unverified
4AarenMSE0.65—Unverified
5RPMixerMSE0.52—Unverified
6ATFNetMSE0.51—Unverified
7MOIRAILargeMSE0.51—Unverified
8AutoformerMSE0.51—Unverified
9SCINetMSE0.5—Unverified
10S-MambaMSE0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1QuerySelectorMSE1.12—Unverified
2TransformerMSE1.11—Unverified
3InformerMSE0.94—Unverified
4GLinearMSE0.59—Unverified
5SCINetMSE0.54—Unverified
6MoLE-DLinearMSE0.51—Unverified
7PRformerMSE0.49—Unverified
8TEFNMSE0.48—Unverified
9DLinearMSE0.47—Unverified
10FiLMMSE0.47—Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerMSE2.66—Unverified
2QuerySelectorMSE2.32—Unverified
3InformerMSE1.67—Unverified
4DLinearMSE0.45—Unverified
5TEFNMSE0.42—Unverified
6MoLE-DLinearMSE0.42—Unverified
7FiLMMSE0.38—Unverified
8MoLE-RLinearMSE0.37—Unverified
9SCINetMSE0.37—Unverified
10PRformerMSE0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerMSE3.18—Unverified
2QuerySelectorMSE3.07—Unverified
3InformerMSE2.34—Unverified
4MoLE-DLinearMSE0.61—Unverified
5DLinearMSE0.61—Unverified
6SCINetMSE0.48—Unverified
7FiLMMSE0.44—Unverified
8TEFNMSE0.43—Unverified
9TiDEMSE0.42—Unverified
10MoLE-RLinearMSE0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1MoLE-DLinearMSE0.45—Unverified
2TEFNMSE0.43—Unverified
3FiLMMSE0.41—Unverified
4PatchTST/64MSE0.41—Unverified
5TiDEMSE0.41—Unverified
6NLinearMSE0.41—Unverified
7DiPE-LinearMSE0.41—Unverified
8DLinearMSE0.41—Unverified
9RLinearMSE0.4—Unverified
10MoLE-RLinearMSE0.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DLinearMSE0.38—Unverified
2TEFNMSE0.38—Unverified
3MoLE-DLinearMSE0.36—Unverified
4FiLMMSE0.36—Unverified
5NLinearMSE0.34—Unverified
6PatchTST/64MSE0.34—Unverified
7MoLE-RLinearMSE0.34—Unverified
8TiDEMSE0.33—Unverified
9PRformerMSE0.33—Unverified
10LTBoost (drop_last=false)MSE0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1DLinearMSE0.29—Unverified
2TEFNMSE0.29—Unverified
3MoLE-DLinearMSE0.29—Unverified
4FiLMMSE0.28—Unverified
5NLinearMSE0.28—Unverified
6TSMixerMSE0.28—Unverified
7DiPE-LinearMSE0.28—Unverified
8PatchTST/64MSE0.27—Unverified
9MoLE-RLinearMSE0.27—Unverified
10TiDEMSE0.27—Unverified
#ModelMetricClaimedVerifiedStatus
1TEFNMSE0.38—Unverified
2MoLE-DLinearMSE0.38—Unverified
3TiDEMSE0.38—Unverified
4MoLE-RLinearMSE0.38—Unverified
5FiLMMSE0.37—Unverified
6PatchTST/64MSE0.37—Unverified
7DiPE-LinearMSE0.37—Unverified
8TSMixerMSE0.37—Unverified
9RLinearMSE0.37—Unverified
10TTMMSE0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1TEFNMSE0.23—Unverified
2DLinearMSE0.22—Unverified