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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/adityalab/pets

공식 코드

Performative Time-Series Forecasting

https://github.com/adityalab/profhit

공식 코드

When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting

https://github.com/adityalab/time-mmd

공식 코드

Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

https://github.com/adlnlp/stockemotions

공식 코드

StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series

https://github.com/aefdz/nnFTS

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On projection methods for functional time series forecasting

https://github.com/aeon-toolkit/aeon

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aeon: a Python toolkit for learning from time series

https://github.com/ahmad-shirazi/DSSRNN

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DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series Analysis

https://github.com/ahmadmak/time-series-optimization-research

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Optimizing Time Series Forecasting: A Comparative Study of Adam and Nesterov Accelerated Gradient on LSTM and GRU networks Using Stock Market data

https://github.com/ai4cts/lightcts

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LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

https://github.com/ai4healthuol/sssd

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The Rise of Diffusion Models in Time-Series Forecasting

https://github.com/aikunyi/amplifier

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Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting

https://github.com/aikunyi/filternet

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FilterNet: Harnessing Frequency Filters for Time Series Forecasting

https://github.com/aikunyi/fouriergnn

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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

https://github.com/ailab-cvc/unireplknet

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UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition

https://github.com/alan-turing-institute/rPSMF

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Probabilistic sequential matrix factorization

https://github.com/alasdairtran/radflow

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Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series

https://github.com/alg-znsy-li/mofe-time

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MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models

https://github.com/ali-forootani/compactformer

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Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks

https://github.com/alipay/rgsl

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Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

https://github.com/amazon-science/unconditional-time-series-diffusion

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Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

https://github.com/ameliawong1996/From_News_to_Forecast

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From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection

https://github.com/aminegha/streampred

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Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach

https://github.com/andreacini/higp

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Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

https://github.com/andreacini/sparse-graph-learning

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Sparse Graph Learning from Spatiotemporal Time Series

https://github.com/anoise/minusformer

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Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals

https://github.com/anthony-frion/aikae

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Augmented Invertible Koopman Autoencoder for long-term time series forecasting

https://github.com/anthony-frion/sentinel2ts

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Koopman Ensembles for Probabilistic Time Series Forecasting

https://github.com/antiingel/correlator

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Correlated daily time series and forecasting in the M4 competition

https://github.com/anuraganands/Non-image-data-classification-with-CNN

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Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis

https://github.com/anwarmaxsum/mantra

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Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks

https://github.com/archon159/RAFT

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Retrieval Augmented Time Series Forecasting

https://github.com/ari-dasci/S-LAM

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Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting

https://github.com/aryan-jadon/regression-loss-functions-in-time-series-forecasting-tensorflow

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A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting

https://github.com/ashfarhangi/aa-forecast

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AA-Forecast: Anomaly-Aware Forecast for Extreme Events

https://github.com/ashfarhangi/covid-19

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A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19

https://github.com/atik-ahamed/timemachine

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TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

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