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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/quilograma/aenbmimocqr

공식 코드

A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting

https://github.com/rabadiah/generalization-capabilities-and-robustness-of-hybrid-machine-learning-models

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Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models

https://github.com/radi-cho/srs21-public-data

공식 코드

Transformers predicting the future. Applying attention in next-frame and time series forecasting

https://github.com/radiantresearch/tsat

공식 코드

Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer

https://github.com/radrumond/timehetnet

공식 코드

Few-Shot Forecasting of Time-Series with Heterogeneous Channels

https://github.com/rafadd/condtsf

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CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting

https://github.com/rajatsen91/deepglo

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Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

https://github.com/rakshitha123/Localised_Ensembles

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Ensembles of Localised Models for Time Series Forecasting

https://github.com/rakshitha123/TSForecasting

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Monash Time Series Forecasting Archive

https://github.com/rakshitha123/WeeklyForecasting

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An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting

https://github.com/rakshitha123/setar_trees

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SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting

https://github.com/raphischer/xpcr

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AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting

https://github.com/rationalspark/jtft

공식 코드

A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting

https://github.com/ratschlab/dpsom

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DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

https://github.com/ravi-chepuri/hybrid_rc_ngrc

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Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems

https://github.com/razvanc92/st-wa

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Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version

https://github.com/realwangjiahao/timereasoner

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Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting

https://github.com/remigenet/SigKAN

공식 코드

SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series

https://github.com/remigenet/TKAT

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A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting

https://github.com/remigenet/TLN

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A Temporal Linear Network for Time Series Forecasting

https://github.com/remigenet/tkan

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TKAN: Temporal Kolmogorov-Arnold Networks

https://github.com/rhymesg/CapsNet

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A Capsule Network for Traffic Speed Prediction in Complex Road Networks

https://github.com/ricardoinaciopt/mast

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Meta-learning and Data Augmentation for Stress Testing Forecasting Models

https://github.com/rjlicata/tie-gcm_rope_preprint

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Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models

https://github.com/robertleppich/rep-net

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Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection

https://github.com/robertleppich/tsrm

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TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation

https://github.com/rogerni/mole

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Mixture-of-Linear-Experts for Long-term Time Series Forecasting

https://github.com/romilbert/samformer

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SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

https://github.com/rose-stl-lab/anomllm

공식 코드

Can LLMs Understand Time Series Anomalies?

https://github.com/rose-stl-lab/copulacpts

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Copula Conformal Prediction for Multi-step Time Series Forecasting

https://github.com/rottenivy/betterbatch

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Better Batch for Deep Probabilistic Time Series Forecasting

https://github.com/rottenivy/dynamic-regression

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Probabilistic Traffic Forecasting with Dynamic Regression

https://github.com/rottenivy/mv_pts_correlatederr

공식 코드

Multivariate Probabilistic Time Series Forecasting with Correlated Errors

https://github.com/rrrussell/caugnn

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Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/rtqichen/torchdiffeq

공식 코드

Neural Ordinary Differential Equations

https://github.com/ruixindingecnu/drformer

공식 코드

DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting

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