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 12011250 of 1609 papers

TitleStatusHype
Wavelet‐attention‐based traffic prediction for smart cities0
Expert Aggregation for Financial Forecasting0
Neural network stochastic differential equation models with applications to financial data forecastingCode1
Smart Data Representations: Impact on the Accuracy of Deep Neural NetworksCode0
Nyström Regularization for Time Series Forecasting0
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting0
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting0
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting0
Coherent Probabilistic Aggregate Queries on Long-horizon ForecastsCode1
Meta-Forecasting by combining Global Deep Representations with Local Adaptation0
Transferable Time-Series Forecasting under Causal Conditional ShiftCode1
LibCity: An Open Library for Traffic PredictionCode2
Predictive Auto-scaling with OpenStack MonascaCode0
A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformersCode0
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data0
Cluster-and-Conquer: A Framework For Time-Series Forecasting0
Contrastive Neural Processes for Self-Supervised LearningCode1
Adversarial attacks against Bayesian forecasting dynamic models0
Power Line Communication and Sensing Using Time Series Forecasting0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!0
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks0
A novel stochastic model based on echo state networks for hydrological time series forecasting0
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series ForecastingCode0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
Time Series Forecasting Using Manifold Learning0
An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter0
Long-term Prediction of Nonlinear Time Series Using Autoencoder and Echo State Networks0
A Study of Aggregation of Long Time-series Input for LSTM Neural Networks0
Causal Triple Attention Time Series Forecasting0
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting0
Automatic Forecasting via Meta-Learning0
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution ShiftCode1
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting0
Modeling Variable Space with Residual Tensor Networks for Multivariate Time Series0
Long-Range Transformers for Dynamic Spatiotemporal ForecastingCode1
Temporal Convolutional Attention Neural Networks for Time Series ForecastingCode1
Deep Learning with Kernel Flow Regularization for Time Series Forecasting0
Modeling Regime Shifts in Multiple Time Series0
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast ModelsCode1
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba0
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Instance-wise Graph-based Framework for Multivariate Time Series ForecastingCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
TCCT: Tightly-Coupled Convolutional Transformer on Time Series ForecastingCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models0
Construction Cost Index Forecasting: A Multi-feature Fusion Approach0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain0
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Benchmark Results

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