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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/dongbeank/cats

공식 코드

Are Self-Attentions Effective for Time Series Forecasting?

https://github.com/duongtrung/time-series-temporal-saliency-patterns

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Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting

https://github.com/durandallee/aceformer

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An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting

https://github.com/dweprinz/CP-TSD

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Conformal time series decomposition with component-wise exchangeability

https://github.com/earthnet2021/earthnet-model-intercomparison-suite

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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

https://github.com/earthnet2021/earthnet-models-pytorch

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Multi-modal learning for geospatial vegetation forecasting

https://github.com/edo-urettini/GAS_Norm

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GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning

https://github.com/egetaga/timepfn

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TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data

https://github.com/egoper/multiple-resolution-tokenization

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Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing

https://github.com/elephaint/pedpf

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Parameter Efficient Deep Probabilistic Forecasting

https://github.com/ennosigaeon/auto-sktime

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auto-sktime: Automated Time Series Forecasting

https://github.com/epu-ntua/deeptsf

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DeepTSF: Codeless machine learning operations for time series forecasting

https://github.com/ericssonresearch/eon

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QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting

https://github.com/esl-epfl/cross-domain-saliency-maps

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Time series saliency maps: explaining models across multiple domains

https://github.com/esl-epfl/cross-domain-saliency-maps-paper

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Time series saliency maps: explaining models across multiple domains

https://github.com/espalemit/vmd-with-ltsf-linear

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Variational Mode Decomposition and Linear Embeddings are What You Need For Time-Series Forecasting

https://github.com/eth-sri/gains

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Efficient Certified Training and Robustness Verification of Neural ODEs

https://github.com/etidav/f1fashiondataset

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HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series

https://github.com/etidav/hermes

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HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series

https://github.com/fabianraisch/GenTL

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GenTL: A General Transfer Learning Model for Building Thermal Dynamics

https://github.com/fekoester/AERes

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Attention-Enhanced Reservoir Computing

https://github.com/feng-li/forecasting-with-optimal-starting-point

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Optimal starting point for time series forecasting

https://github.com/filippomb/ensemble-conformalized-quantile-regression

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Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

https://github.com/fmlyd/fsmlp

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FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency Domain

https://github.com/fmorenopino/SAAM

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Deep Autoregressive Models with Spectral Attention

https://github.com/forgee-master/HADL

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HADL Framework for Noise Resilient Long-Term Time Series Forecasting

https://github.com/frankl1/piezoforecast

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Experimental study of time series forecasting methods for groundwater level prediction

https://github.com/fuyuanlyu/rts-pno

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Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting

https://github.com/gabrielspadon/ReGENN

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Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning

https://github.com/gantheory/TPA-LSTM

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Temporal Pattern Attention for Multivariate Time Series Forecasting

https://github.com/gaohaozhi/DE-TSMCL

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Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning

https://github.com/garethmd/nnts

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Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting

https://github.com/gcliu1982/CNNM

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Recovery of Future Data via Convolution Nuclear Norm Minimization

https://github.com/gcliu1982/LbCNNM

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Time Series Forecasting via Learning Convolutionally Low-Rank Models

https://github.com/gestaltcogteam/basicts

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Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

https://github.com/gestaltcogteam/blast

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BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models

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