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Time Series Forecasting

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하위 태스크: 6
평가표: 86
보존된 평가 기록: 538

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).

<span style="color:grey; opacity: 0.6">( Image credit: ThaiBinh Nguyen )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Graph-Machine-Learning-Group/peakweather-wind-forecasting

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PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning

https://github.com/Graph-and-Geometric-Learning/MTBench

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MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering

https://github.com/HadiJahanshahi/Bug-Number-Prediction

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Predicting the Number of Reported Bugs in a Software Repository

https://github.com/Hank0626/WFTNet

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WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

https://github.com/Hannibal046/GridTST

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Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers

https://github.com/HansikaPH/time-series-forecasting

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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

https://github.com/HansikaPH/tssimulate

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Global Models for Time Series Forecasting: A Simulation Study

https://github.com/Hritikbansal/MultivariateTimeSeries_Generalization

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Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models

https://github.com/INESCTEC/budget-constrained-collaborative-forecasting-market

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Budget-constrained Collaborative Renewable Energy Forecasting Market

https://github.com/JIANGYUE61610306/SAGDFN

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SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting

https://github.com/JLDeng/SCNN

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Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

https://github.com/JLDeng/ST-Norm

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A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting

https://github.com/JohnsonJiang1996/AdvAttack_LLM4TS

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Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting

https://github.com/KIT-IAI/EasyMLServe

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EasyMLServe: Easy Deployment of REST Machine Learning Services

https://github.com/Kaimaoge/IGNNK

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Inductive Graph Neural Networks for Spatiotemporal Kriging

https://github.com/Kowsher/LLMMixer

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LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting

https://github.com/LSY-Cython/CCDM

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Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting

https://github.com/LSY-Cython/DiffPLF

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DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load

https://github.com/LeiBAI/AGCRN

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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

https://github.com/Levi-Ackman/Leddam

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Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling

https://github.com/Lysarthas/StochDiff

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Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting

https://github.com/MIMUW-RL/Unified-Long-Horizon-Time-Series-Benchmark

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Unified Long-Term Time-Series Forecasting Benchmark

https://github.com/MPC-Dynamic/Growth

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Modeling the Dynamics of Growth in Master-Planned Communities

https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/PyTorch/TimeSeriesPredictionPlatform

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TSPP: A Unified Benchmarking Tool for Time-series Forecasting

https://github.com/Nakols/HFformerV2

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Exploring the Advantages of Transformers for High-Frequency Trading

https://github.com/NathanDoumeche/WeaKL

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Forecasting time series with constraints

https://github.com/OrigamiSL/TCCT2021

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TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting

https://github.com/PalaceTony/ProGen

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ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations

https://github.com/Peng154/Delay-Embedding-based-Forecast-Machine

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DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation

https://github.com/Quan-Zhou/Fairness-in-Learning-of-LDS

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Fairness in Forecasting of Observations of Linear Dynamical Systems

https://github.com/RRRussell/CauGNN/tree/main/MTS_TEGNN-master

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Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/RichardBean/IEEE-Predict-Optimize-Challenge

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Methodology for forecasting and optimization in IEEE-CIS 3rd Technical Challenge

https://github.com/SalesforceAIResearch/uni2ts

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Unified Training of Universal Time Series Forecasting Transformers

https://github.com/Secure-and-Intelligent-Systems-Lab/WPMixer

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WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting

https://github.com/ServiceNow/regions-of-reliability

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Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

https://github.com/ServiceNow/tactis

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TACTiS: Transformer-Attentional Copulas for Time Series

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.