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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/keytoyze/visionts

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VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

https://github.com/khairulislam/Timeseries-Explained

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WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models

https://github.com/khegazy/Powerformer

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Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting

https://github.com/kimanki/tafas

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Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

https://github.com/kimmeen/awesome-gnn4ts

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A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

https://github.com/kit-iai/smartdatarepresentations

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Smart Data Representations: Impact on the Accuracy of Deep Neural Networks

https://github.com/kittimatechulajata/predicting-outcomes-in-two-player-games-with-lstm

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Predicting Outcomes in Video Games with Long Short Term Memory Networks

https://github.com/kloetergensc/functional-latent_dynamics

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Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

https://github.com/kurbanintelligencelab/multdbg-time-series-library

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Multivariate de Bruijn Graphs: A Symbolic Graph Framework for Time Series Forecasting

https://github.com/kutaytire/retrieval-augmented-time-series-forecasting

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Retrieval Augmented Time Series Forecasting

https://github.com/kwuking/TimeMixer

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TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

https://github.com/laiguokun/LSTNet

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

https://github.com/laiguokun/multivariate-time-series-data

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

https://github.com/lancelotxwx/swift

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SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting

https://github.com/larsbentsen/fftransformer

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Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures

https://github.com/ledererlab/deepcar

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The DeepCAR Method: Forecasting Time-Series Data That Have Change Points

https://github.com/leejoonhun/fan-beats

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Feature-aligned N-BEATS with Sinkhorn divergence

https://github.com/leo-vk/proenfo

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Benchmarks and Custom Package for Energy Forecasting

https://github.com/leobix/adaptive_ensemble

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Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach

https://github.com/leopold2333/waverora

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WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting

https://github.com/levi-ackman/lino

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LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting

https://github.com/levi-ackman/refocus

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ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting

https://github.com/liam-sbhoo/tabpfn-time-series

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The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features

https://github.com/linkedin/greykite

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Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

https://github.com/liuxu77/unitime

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UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

https://github.com/liuzh-19/esg

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Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

https://github.com/liyaguang/DCRNN

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

https://github.com/ljc-fvnr/arma-attention

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Autoregressive Moving-average Attention Mechanism for Time Series Forecasting

https://github.com/ljc-fvnr/structural-aligned-mixture-of-var

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Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting

https://github.com/lluism/counterfacts

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Probing the Robustness of Time-series Forecasting Models with CounterfacTS

https://github.com/lmh9507/casa

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CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting

https://github.com/lqzxt/time-r1

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Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

https://github.com/lrq577/faith

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FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting

https://github.com/lss-1138/SegRNN

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SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

https://github.com/lss-1138/SparseTSF

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SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters

https://github.com/lunaaa95/mou

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Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need

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