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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/chenzrg/fredformer

공식 코드

Fredformer: Frequency Debiased Transformer for Time Series Forecasting

https://github.com/chiyuzhang94/contrastive_learning_time-series_e2e

공식 코드

What Constitutes Good Contrastive Learning in Time-Series Forecasting?

https://github.com/cilab-ufersa/period_cycle_prediction

공식 코드

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

https://github.com/citlab/fed_challenge

공식 코드

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

https://github.com/citymind-lab/neurips24-attraos

공식 코드

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective

https://github.com/claudiashu/sonnet

공식 코드

Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting

https://github.com/clinicalml/structuredinference

공식 코드

Structured Inference Networks for Nonlinear State Space Models

https://github.com/complex-ai-lab/ncc

공식 코드

Neural Conformal Control for Time Series Forecasting

https://github.com/crabtain959/synbeats

공식 코드

Forecasting Algorithms for Causal Inference with Panel Data

https://github.com/craftintsf/craft

공식 코드

CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness

https://github.com/crseu/llmtime_vs_arima

공식 코드

An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting

https://github.com/cruiseresearchgroup/xxltraffic

공식 코드

XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation

https://github.com/cstcloudops/cmos

공식 코드

CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

https://github.com/csun365/mosquito-habitat-prediction

공식 코드

Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning

https://github.com/csyyn/vcformer

공식 코드

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

https://github.com/cure-lab/ltsf-linear

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Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting

https://github.com/cvxgrp/joint-lrsm

공식 코드

Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models

https://github.com/damitkwr/ESRNN-GPU

공식 코드

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm

https://github.com/damo-di-ml/one_fits_all

공식 코드

One Fits All:Power General Time Series Analysis by Pretrained LM

https://github.com/dandelionym/cdvgm

공식 코드

Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction

https://github.com/datadog/toto

공식 코드

This Time is Different: An Observability Perspective on Time Series Foundation Models

https://github.com/davidanastasiu/dan

공식 코드

Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting

https://github.com/daxin007/armd

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Auto-Regressive Moving Diffusion Models for Time Series Forecasting

https://github.com/daxin007/client

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Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting

https://github.com/dbvis-ukon/ts-mule

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TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

https://github.com/dc-research/tempo

공식 코드

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

https://github.com/ddz16/Preformer

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Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting

https://github.com/decisionintelligence/cs4ts

공식 코드

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

https://github.com/decisionintelligence/duet

공식 코드

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

https://github.com/decisionintelligence/k2vae

공식 코드

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

https://github.com/decisionintelligence/pathformer

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Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting

https://github.com/decisionintelligence/tfb

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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

https://github.com/deepkashiwa20/Urban_Concept_Drift

공식 코드

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

https://github.com/deepkashiwa20/megacrn

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Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

https://github.com/djlee1208/bsa_2024

공식 코드

Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting

https://github.com/dmirlab-group/gca

공식 코드

Transferable Time-Series Forecasting under Causal Conditional Shift

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