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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/StatNLP/mlhc_2024_prediction_of_causes

공식 코드

Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting

https://github.com/Tingji2419/SeqFusion

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SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting

https://github.com/USC-InfoLab/busyness-graph-neural-network

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Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

https://github.com/UniqueoneZ/AverageLinear

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AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging

https://github.com/VeritasYin/STGCN_IJCAI-18

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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

https://github.com/WenjieDu/PyGrinder

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PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/WenjieDu/PyPOTS

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Recurrent Neural Networks for Multivariate Time Series with Missing Values

https://github.com/WenjieDu/TSDB

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PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/Wentao-Gao/TSI-forcasting

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TSI: A Multi-View Representation Learning Approach for Time Series Forecasting

https://github.com/WuQiangXDU/Peri-midFormer

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Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series

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SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting

https://github.com/XiudingCai/MambaTS-pytorch

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MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting

https://github.com/YangLIN1997/AMLNet-DSAA2023

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AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting

https://github.com/YangLIN1997/Progressive-neural-network-for-multi-horizon-time-series-forecasting

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Progressive Neural Network for Multi-Horizon Time Series Forecasting

https://github.com/YangLIN1997/SSDNet-ICDM2021

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SSDNet: State Space Decomposition Neural Network for Time Series Forecasting

https://github.com/Yitiann/MTST

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Multi-resolution Time-Series Transformer for Long-term Forecasting

https://github.com/Yu-Group/covid19-severity-prediction

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Curating a COVID-19 data repository and forecasting county-level death counts in the United States

https://github.com/YukiBear426/AEFIN

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Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism

https://github.com/YukinoAsuna/SAMBA

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Simplified Mamba with Disentangled Dependency Encoding for Long-Term Time Series Forecasting

https://github.com/YuliaRubanova/latent_ode

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Latent ODEs for Irregularly-Sampled Time Series

https://github.com/Yunbo426/MIM

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Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

https://github.com/YuxiaoLuo0013/TFDNet

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TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting

https://github.com/Z-GCNETs/Z-GCNETs

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Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

https://github.com/Zeying-Gong/PatchMixer

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PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting

https://github.com/ZhuoLinLi-shu/SDGL

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Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling

https://github.com/Zymrael/PortHamiltonianNN

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Port-Hamiltonian Approach to Neural Network Training

https://github.com/ab9mamun/movesense

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Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

https://github.com/abacusai/forecastpfn

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ForecastPFN: Synthetically-Trained Zero-Shot Forecasting

https://github.com/abdo-aar/quack-tsf

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QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting

https://github.com/abenechehab/adapts

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AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

https://github.com/abrarmajeedi/DeepEDM

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LETS Forecast: Learning Embedology for Time Series Forecasting

https://github.com/adis98/stv

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Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning

https://github.com/adityalab/camul

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CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

https://github.com/adityalab/foil

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Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning

https://github.com/adityalab/mm-tsflib

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Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

https://github.com/adityalab/opentimer

공식 코드

Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights

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