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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/smtmnfg/tsfm

공식 코드

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction

https://github.com/stanliu96/RATD

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Retrieval-Augmented Diffusion Models for Time Series Forecasting

https://github.com/starfruit007/ipnn

공식 코드

Indeterminate Probability Theory

https://github.com/statmixedml/hyper-trees

공식 코드

Forecasting with Hyper-Trees

https://github.com/stefanbloemheuvel/gcntimeseriesregression

공식 코드

Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

https://github.com/stijnverdenius/lat-pfn

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LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting

https://github.com/stitsyuk/xpatch

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xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition

https://github.com/sunbus100/FreqMoE-main

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FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts

https://github.com/swang-song/ve

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VE: Modeling Multivariate Time Series Correlation with Variate Embedding

https://github.com/sydney-machine-learning/deeplearning-decadalworldeconomy

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Recursive deep learning framework for forecasting the decadal world economic outlook

https://github.com/syrgithub/hcan

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Hierarchical Classification Auxiliary Network for Time Series Forecasting

https://github.com/syrgithub/tfps

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Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift

https://github.com/t-rizvi/GLinear

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Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction

https://github.com/taohan10200/weather-5k

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How far are today's time-series models from real-world weather forecasting applications?

https://github.com/tetrzim/robust-probabilistic-forecasting

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Robust Probabilistic Time Series Forecasting

https://github.com/thorhojhus/ssl_fts

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Self-Supervised Learning for Time Series: A Review & Critique of FITS

https://github.com/thuml/AutoTimes

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AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

https://github.com/thuml/Large-Time-Series-Model

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Timer: Generative Pre-trained Transformers Are Large Time Series Models

https://github.com/thuml/autoformer

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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

https://github.com/thuml/dynamical-diffusion

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Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models

https://github.com/thuml/iTransformer

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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

https://github.com/thuml/timexer

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TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

https://github.com/tianjinyellow/spam-optimizer

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SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

https://github.com/tib-k330/pmm-net

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PMM-Net: Single-stage Multi-agent Trajectory Prediction with Patching-based Embedding and Explicit Modal Modulation

https://github.com/time-moe/time-moe

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Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

https://github.com/time-series-foundation-models/lag-llama

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Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

https://github.com/tims2d/times2d

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Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting

https://github.com/tongjifinlab/fintsbenchmark

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FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

https://github.com/tongnie/c-lora

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Channel-Aware Low-Rank Adaptation in Time Series Forecasting

https://github.com/troubadour000/amd

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Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting

https://github.com/troubadour000/timefilter

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TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting

https://github.com/ts-xai/whyforecast

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TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting

https://github.com/tsfhd2024/tsf-hd

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A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge

https://github.com/uber/ludwig

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Ludwig: a type-based declarative deep learning toolbox

https://github.com/unnir/cancelout

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Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting

https://github.com/usail-hkust/aseer

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Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

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