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 1–50 of 1609 papers

TitleStatusHype
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous VariablesCode7
Logo-LLM: Local and Global Modeling with Large Language Models for Time Series ForecastingCode7
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series ForecastingCode6
A decoder-only foundation model for time-series forecastingCode6
iTransformer: Inverted Transformers Are Effective for Time Series ForecastingCode6
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series ForecastingCode6
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting ModelsCode5
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal RepresentationsCode5
A Time Series is Worth 64 Words: Long-term Forecasting with TransformersCode5
aeon: a Python toolkit for learning from time seriesCode5
TimeMixer: Decomposable Multiscale Mixing for Time Series ForecastingCode5
Long-term Forecasting with TiDE: Time-series Dense EncoderCode5
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image RecognitionCode5
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting MethodsCode5
Unified Training of Universal Time Series Forecasting TransformersCode5
Timer: Generative Pre-trained Transformers Are Large Time Series ModelsCode4
Timer-XL: Long-Context Transformers for Unified Time Series ForecastingCode4
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of ExpertsCode4
Time-LLM: Time Series Forecasting by Reprogramming Large Language ModelsCode4
Efficient Automated Deep Learning for Time Series ForecastingCode4
Are Transformers Effective for Time Series Forecasting?Code4
Zero-shot forecasting of chaotic systemsCode4
UniTS: A Unified Multi-Task Time Series ModelCode4
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time SeriesCode4
TimeMachine: A Time Series is Worth 4 Mambas for Long-term ForecastingCode3
The Rise of Diffusion Models in Time-Series ForecastingCode3
FilterNet: Harnessing Frequency Filters for Time Series ForecastingCode3
The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple FeaturesCode3
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity AnalysisCode3
FAN: Fourier Analysis NetworksCode3
Are Language Models Actually Useful for Time Series Forecasting?Code3
This Time is Different: An Observability Perspective on Time Series Foundation ModelsCode3
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series TransformerCode3
Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series ForecastingCode3
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core FusionCode3
OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed DomainCode3
Ludwig: a type-based declarative deep learning toolboxCode3
MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K ParametersCode3
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series ForecastingCode3
MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series ForecastingCode3
N-BEATS: Neural basis expansion analysis for interpretable time series forecastingCode3
SparseTSF: Modeling Long-term Time Series Forecasting with 1k ParametersCode3
Probabilistic Forecasting with Temporal Convolutional Neural NetworkCode3
Is Mamba Effective for Time Series Forecasting?Code3
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic PatternsCode3
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series ForecastingCode3
Lag-Llama: Towards Foundation Models for Probabilistic Time Series ForecastingCode3
GIFT-Eval: A Benchmark For General Time Series Forecasting Model EvaluationCode3
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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