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

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Showing 301–350 of 1609 papers

TitleStatusHype
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond—0
KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction—0
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization—0
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning—0
WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning ModelsCode1
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data—0
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data—0
FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency DomainCode0
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
Signal Processing over Time-Varying Graphs: A Systematic Review—0
Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data—0
An Adversarial Learning Approach to Irregular Time-Series Forecasting—0
MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual UnmixingCode0
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting—0
Disentangled Interpretable Representation for Efficient Long-term Time Series ForecastingCode1
Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms—0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
Enhancing Project Performance Forecasting using Machine Learning Techniques—0
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting—0
Tackling Data Heterogeneity in Federated Time Series Forecasting—0
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting—0
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption—0
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs—0
A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors—0
Knowledge-enhanced Transformer for Multivariate Long Sequence Time-series Forecasting—0
FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework—0
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
Retrieval Augmented Time Series ForecastingCode2
ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting—0
Leveraging LSTM for Predictive Modeling of Satellite Clock Bias—0
Local vs. Global Models for Hierarchical Forecasting—0
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges—0
A Survey on Kolmogorov-Arnold Network—0
Peri-midFormer: Periodic Pyramid Transformer for Time Series AnalysisCode1
Series-to-Series Diffusion Bridge Model—0
EffiCANet: Efficient Time Series Forecasting with Convolutional Attention—0
Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data—0
Fully Automated Correlated Time Series Forecasting in Minutes—0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
A Mamba Foundation Model for Time Series Forecasting—0
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series TransformerCode3
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting—0
FilterNet: Harnessing Frequency Filters for Time Series ForecastingCode3
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics—0
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential EquationsCode0
Text2Freq: Learning Series Patterns from Text via Frequency Domain—0
RAM: Replace Attention with MLP for Efficient Multivariate Time Series Forecasting—0
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All datasetsETTh1 (336) MultivariateETTh1 (720) MultivariateETTh2 (336) MultivariateETTh2 (720) MultivariateETTh1 (192) MultivariateETTh2 (192) MultivariateETTh2 (96) MultivariateETTh1 (96) MultivariateWeather (192)ETTh1 (720) UnivariateWeather (96)Electricity (96)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InformerMSE0.88—Unverified
2QuerySelectorMSE0.85—Unverified
3TransformerMSE0.83—Unverified
4AarenMSE0.65—Unverified
5RPMixerMSE0.52—Unverified
6MOIRAILargeMSE0.51—Unverified
7ATFNetMSE0.51—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
4DLinearMSE0.61—Unverified
5MoLE-DLinearMSE0.61—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
9LTBoost (drop_last=false)MSE0.33—Unverified
10PRformerMSE0.33—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