Papers with Code 아카이브
Time Series Forecasting
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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/wpower12/COVID-19-USSTN
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
https://github.com/xingyu617/SimTS_Representation_Learning
https://github.com/xinzezhang/timeseriesforecasting-torch
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
https://github.com/xlwang233/pytorch-dcrnn
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/xqnwang/fuma
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/xuanqing94/torchdiffeq
https://github.com/xuehaouwa/lm-load-forecasting
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
https://github.com/xwinxu/bayesde
https://github.com/xwinxu/bayesian-sde
https://github.com/yangyucheng000/University/tree/main/model-1/rwkv
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
https://github.com/yangyucheng000/ascend_gru
https://github.com/yanle87937051/Transformer-TCCT2021
TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting
https://github.com/yash-nishaant/Seq2Seq-Chatbot
https://github.com/yjlolo/pytorch-deep-markov-model
Structured Inference Networks for Nonlinear State Space Models
https://github.com/yscope75/CS2225.CH2001020
https://github.com/yuyama137/influenza
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
https://github.com/zachysun/taxi_traffic_benchmark
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
https://github.com/zekun-cai/koodos
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
https://github.com/zhaozijun102548/multivariate_prediction_master
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
https://github.com/zhouhaoyi/ETDataset
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
https://github.com/zhouhaoyi/Informer2020
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
https://github.com/zhuqunxi/My_numpy_NODE
https://github.com/zhykoties/DeepAR
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
https://github.com/ziyulu-uw/causalformer
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
https://github.com/zyf0220/pattern_recognize
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.