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 651700 of 1609 papers

TitleStatusHype
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series ForecastingCode3
Using ARIMA to Predict the Expansion of Subscriber Data Consumption0
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core FusionCode3
Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approach0
Exploring the Role of Token in Transformer-based Time Series Forecasting0
High Significant Fault Detection in Azure Core Workload Insights0
The impact of data set similarity and diversity on transfer learning success in time series forecasting0
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series ForecastingCode2
Some variation of COBRA in sequential learning setup0
Supervised Autoencoder MLP for Financial Time Series Forecasting0
Multiple model estimation under perspective of random-fuzzy dual interpretation of unknown uncertaintyCode0
From Similarity to Superiority: Channel Clustering for Time Series ForecastingCode0
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection0
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting MethodsCode5
Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy0
Gegenbauer Graph Neural Networks for Time-varying Signal ReconstructionCode0
IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction0
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series ForecastingCode1
An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock ForecastingCode2
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-AdaptCode2
Grey-informed neural network for time-series forecasting0
Explaining deep learning models for ozone pollution prediction via embedded feature selectionCode0
An Analysis of Linear Time Series Forecasting ModelsCode1
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models0
HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting0
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)Code0
Is Mamba Effective for Time Series Forecasting?Code3
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting0
Chain-structured neural architecture search for financial time series forecasting0
TimeMachine: A Time Series is Worth 4 Mambas for Long-term ForecastingCode3
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer0
Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting0
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-TuningCode2
Koopman Ensembles for Probabilistic Time Series ForecastingCode0
S^2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series ForecastingCode2
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning ProcessCode2
RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting0
Hyperparameter Tuning MLPs for Probabilistic Time Series ForecastingCode0
Probing the Robustness of Time-series Forecasting Models with CounterfacTSCode0
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting0
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical SystemsCode0
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous VariablesCode1
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis0
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling0
UniTS: A Unified Multi-Task Time Series ModelCode4
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous VariablesCode7
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting0
Generative Pretrained Hierarchical Transformer for Time Series ForecastingCode1
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equationsCode0
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting0
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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