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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/sakurfall/dateformer

공식 코드

Dateformer: Time-modeling Transformer for Longer-term Series Forecasting

https://github.com/salesforce/CoST

공식 코드

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

https://github.com/salesforce/deeptime

공식 코드

Learning Deep Time-index Models for Time Series Forecasting

https://github.com/salesforce/fsnet

공식 코드

Learning Fast and Slow for Online Time Series Forecasting

https://github.com/salesforce/online_conformal

공식 코드

Improved Online Conformal Prediction via Strongly Adaptive Online Learning

https://github.com/salesforceairesearch/gift-eval

공식 코드

GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation

https://github.com/salesforceairesearch/pretrain-time-series-cloudops

공식 코드

Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

https://github.com/salvaRC/El-GNNino

공식 코드

Graph Neural Networks for Improved El Niño Forecasting

https://github.com/sanjaylopa22/EDformer-Feature-Importance

공식 코드

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions

https://github.com/sanjaylopa22/FANTF

공식 코드

Enhancing Time Series Forecasting with Fuzzy Attention-Integrated Transformers

https://github.com/sanjaylopa22/QCAAPatchTF

공식 코드

Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting

https://github.com/sdfk72auga1kwsi/aalf

공식 코드

AALF: Almost Always Linear Forecasting

https://github.com/sebasutp/trajectory_forcasting

공식 코드

Real Time Trajectory Prediction Using Deep Conditional Generative Models

https://github.com/secilia-cxy/softs

공식 코드

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

https://github.com/semink/lsdlm

공식 코드

Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs

https://github.com/senyuanya/ldenet

공식 코드

Neural network stochastic differential equation models with applications to financial data forecasting

https://github.com/sepkfr/fine-grained-gaussian-process-forcating

공식 코드

Fine-grained Forecasting Models Via Gaussian Process Blurring Effect

https://github.com/serval-uni-lu/hypernetworks-time-series

공식 코드

Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks

https://github.com/servicenow/context-is-key-forecasting

공식 코드

Context is Key: A Benchmark for Forecasting with Essential Textual Information

https://github.com/shangzongjiang/Ada-MSHyper

공식 코드

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting

https://github.com/shangzongjiang/MSHyper

공식 코드

MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting

https://github.com/shangzongjiang/SNAS4MTF

공식 코드

Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

https://github.com/shangzongjiang/magnn

공식 코드

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

https://github.com/shashankgupta01/exogenous

공식 코드

Anticipating dengue outbreaks using a novel hybrid ARIMA-ARNN model with exogenous variables

https://github.com/sheoyon-jhin/contime

공식 코드

Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization

https://github.com/sherry-xu/deep-switching-state-space-model

공식 코드

Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

https://github.com/shivaniarbat/wgan-gp-transformer

공식 코드

Wasserstein Adversarial Transformer for Cloud Workload Prediction

https://github.com/shiweiguo1995/scformer

공식 코드

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting

https://github.com/simenkristoff/siamtst

공식 코드

SiamTST: A Novel Representation Learning Framework for Enhanced Multivariate Time Series Forecasting applied to Telco Networks

https://github.com/sindhura97/STraTS

공식 코드

Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

https://github.com/sintel-dev/sigllm

공식 코드

Large language models can be zero-shot anomaly detectors for time series?

https://github.com/sir-lab/linear-forecasting

공식 코드

An Analysis of Linear Time Series Forecasting Models

https://github.com/sjtu-dmtai/lift

공식 코드

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

https://github.com/sjtu-dmtai/onlinetsf

공식 코드

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

https://github.com/sjtu-quant/lift

공식 코드

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

https://github.com/smallGum/MLCNN-Multivariate-Time-Series

공식 코드

Towards Better Forecasting by Fusing Near and Distant Future Visions

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