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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/HosseinEbrahimiK/TrajectoryNet-Reproducing

Neural Ordinary Differential Equations

https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN

https://github.com/IBM/UQ360

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

https://github.com/IDSIA/gpforecasting

Time series forecasting with Gaussian Processes needs priors

https://github.com/IKKIM00/multi-horizon-forecasting-comparison-between-TFT-and-DL-methods

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/IKKIM00/stock_and_pm2.5_prediction_using_TFT

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/IvanVassi/Seq2Seq-w-attention-dialogue-model

Sequence to Sequence Learning with Neural Networks

https://github.com/JRC1995/Abstractive-Summarization

Sequence to Sequence Learning with Neural Networks

https://github.com/JSeam2/Neural-Ordinary-Differential-Equations

Neural Ordinary Differential Equations

https://github.com/JonathanRayner/Kaggle-Predict-Sales

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/Junghwan-brian/SDE-Net

Neural Ordinary Differential Equations

https://github.com/KKallidromitis/contr_np

Contrastive Neural Processes for Self-Supervised Learning

https://github.com/KalleBylin/temporal-fusion-transformers

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/KalleBylin/tft_webapp

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/KasperGroesLudvigsen/influenza_transformer/blob/main/README.md

Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

https://github.com/KimMeen/DCRNN

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

https://github.com/KrishnaswamyLab/ImageFlowNet

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

https://github.com/LJC-FVNR/CATS

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables

https://github.com/Lauu1023/ODENet

Neural Ordinary Differential Equations

https://github.com/Ldhlwh/Latent-ODE

Latent ODEs for Irregularly-Sampled Time Series

https://github.com/LeeSH825/TemporalFusionTransformer

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/LeiBAI/STG2Seq

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

https://github.com/LeonardoBerti07/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis

Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

https://github.com/LiamMaclean216/Any-Coin-TFN

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/LiamMaclean216/Pytorch-Chatbot

Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

https://github.com/LiamMaclean216/Pytorch-Transfomer

Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

https://github.com/LiamMaclean216/Temporal-Fusion-Transformer

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/Liut2016/AwesomeDeepLearningGuidance-TimeSeriesData

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

https://github.com/MLD3/Deep-Residual-Time-Series-Forecasting

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

https://github.com/ManjunathAdi/Seq2Seq_RNN

Deep and Confident Prediction for Time Series at Uber

https://github.com/Maple728/MTNet

A Memory-Network Based Solution for Multivariate Time-Series Forecasting

https://github.com/MarigoldJ/TFT_application

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

https://github.com/MatFtDev149/Anomaly-Forecast-and-Detection

AA-Forecast: Anomaly-Aware Forecast for Extreme Events

https://github.com/Meatssauce/SCINet

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction

https://github.com/MeetGandhi/Reconstruction-of-Trajectory-recorded-with-Missing-Markers

Latent ODEs for Irregularly-Sampled Time Series

https://github.com/MichaelKonobeev/neuralode-rl

Neural Ordinary Differential Equations

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