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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://bitbucket.org/dtolpin/wigp

공식 코드

Warped Input Gaussian Processes for Time Series Forecasting

https://git.opendfki.de/koochali/forgan

공식 코드

Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN

https://github.com/18kiran12/Yformer-Time-Series-Forecasting

공식 코드

Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting

https://github.com/18kiran12/tsbench

공식 코드

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

https://github.com/2448845600/kan4tsf

공식 코드

KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?

https://github.com/2noamk/nft

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Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform

https://github.com/772435284/transformers_versus_lstms_for_electronic_trading

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Transformers versus LSTMs for electronic trading

https://github.com/ACAT-SCUT/CycleNet

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CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

https://github.com/ACAT-SCUT/PETformer

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PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer

https://github.com/ACAT-SCUT/TQNet

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Temporal Query Network for Efficient Multivariate Time Series Forecasting

https://github.com/AIStream-Peelout/flow-forecast

공식 코드

FlowDB a large scale precipitation, river, and flash flood dataset

https://github.com/AdityaLab/EpiFNP

공식 코드

When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

https://github.com/AdityaLab/HAILS

공식 코드

Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity

https://github.com/Alebuenoaz/LSTM-and-GRU-Time-Series-Forecasting

공식 코드

Forecasting with Deep Learning

https://github.com/Alrash/TimeCHEAT

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TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis

https://github.com/AntonioLiu97/llmICL

공식 코드

LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law

https://github.com/ArthurLeroy/MAGMA

공식 코드

MAGMA: Inference and Prediction with Multi-Task Gaussian Processes

https://github.com/Axect/HyperbolicLR

공식 코드

HyperbolicLR: Epoch insensitive learning rate scheduler

https://github.com/BorealisAI/dcf

공식 코드

DynaConF: Dynamic Forecasting of Non-Stationary Time Series

https://github.com/CCaribe9/HQR-WACI

공식 코드

Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference

https://github.com/ChangWeiTan/TSRegression

공식 코드

Monash University, UEA, UCR Time Series Extrinsic Regression Archive

https://github.com/ChenLiu-1996/ImageFlowNet

공식 코드

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

https://github.com/ChernovAndrey/chronos-forecasting-wasserstein

공식 코드

Fine-Tuning a Time Series Foundation Model with Wasserstein Loss

https://github.com/ClaudiaShu/DeformTime

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DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting

https://github.com/D-ST-Sword/ATD

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Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method

https://github.com/DMIRLAB-Group/LSTD

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Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting

https://github.com/Daikon-Sun/AdjustAutocorrelation

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Adjusting for Autocorrelated Errors in Neural Networks for Time Series

https://github.com/Daniela-Shereen/GBRT-for-TSF

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Do We Really Need Deep Learning Models for Time Series Forecasting?

https://github.com/DentonJC/DIT

공식 코드

DIT4BEARs Smart Roads Internship

https://github.com/DiTEC-project/DiTEC_WDN_dataset

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DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks

https://github.com/Dilfiraa/PS_Loss

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Patch-wise Structural Loss for Time Series Forecasting

https://github.com/EduProgram/ePL-KRLS-FSM_plus

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Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach

https://github.com/Flawless1202/Non-AR-Spatial-Temporal-Transformer

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NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting

https://github.com/ForestsKing/GLAFF

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Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective

https://github.com/GRYGY1215/Dozerformer

공식 코드

Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting

https://github.com/GRYGY1215/MTPNet

공식 코드

Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting

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