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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/autogluon/autogluon

공식 코드

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

https://github.com/automl/Auto-PyTorch

공식 코드

Efficient Automated Deep Learning for Time Series Forecasting

https://github.com/automl/mamba4cast

공식 코드

Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models

https://github.com/awikner/denpool

공식 코드

Attention-Based Ensemble Pooling for Time Series Forecasting

https://github.com/awslabs/gluonts

공식 코드

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

https://github.com/azencot-group/gatlm

공식 코드

Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning

https://github.com/badripatro/mamba360

공식 코드

Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

https://github.com/beginner-sketch/gmrl

공식 코드

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

https://github.com/ben-hayes/sinusoidal-gradient-descent

공식 코드

Sinusoidal Frequency Estimation by Gradient Descent

https://github.com/bennytmt/llmsfortimeseries

공식 코드

Are Language Models Actually Useful for Time Series Forecasting?

https://github.com/bennytmt/ts_models

공식 코드

Are Language Models Actually Useful for Time Series Forecasting?

https://github.com/berserkhmdvhb/THESIS-MS-Asset-Price-Forecast

공식 코드

Asset Price Forecasting using Recurrent Neural Networks

https://github.com/blacksnail789521/LLM4TS

공식 코드

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

https://github.com/blacksnail789521/timedrl

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TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

https://github.com/bonaldli/spatiotemporal-ts-representation-learning

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Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping

https://github.com/borealisai/scaleformer

공식 코드

Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

https://github.com/boreshkinai/fc-gaga

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FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting

https://github.com/bperezorozco/ordinal_tsf

공식 코드

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

https://github.com/brinkl/graphhetnet

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Few-shot human motion prediction for heterogeneous sensors

https://github.com/bycnfz/weather2k

공식 코드

Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations

https://github.com/c-lyu/traffic4cast2022-tse

공식 코드

Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting

https://github.com/capsar/the-rise-of-diffusion-models-in-time-series-forecasting

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The Rise of Diffusion Models in Time-Series Forecasting

https://github.com/capsar/ts-inverse

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TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models

https://github.com/capwidow/w-transformer

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W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting

https://github.com/catherinehao/timetuner

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TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations

https://github.com/ccaribe9/adaptstdepf

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An adaptive standardisation methodology for Day-Ahead electricity price forecasting

https://github.com/cchallu/n-hits

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N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

https://github.com/cchallu/spectranet

공식 코드

SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data

https://github.com/cdjellen/otbench

공식 코드

Effective Benchmarks for Optical Turbulence Modeling

https://github.com/chaoshangcs/GTS

공식 코드

Discrete Graph Structure Learning for Forecasting Multiple Time Series

https://github.com/chariharasuthan/qultsf

공식 코드

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning

https://github.com/chengqingyu/ginar

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GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing

https://github.com/chengyui/sumformer

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Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach

https://github.com/chenpopper/optimal_timing_tsf

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Deep Optimal Timing Strategies for Time Series

https://github.com/chenxiliu-hnu/timecma

공식 코드

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

https://github.com/chenxiliu-hnu/timekd

공식 코드

Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation

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