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Time Series Analysis

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하위 태스크: 12
평가표: 3
보존된 평가 기록: 14

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

<span style="color:grey; opacity: 0.6">( Image credit: Autoregressive CNNs for Asynchronous Time Series )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/VeritasYin/STGCN_IJCAI-18

공식 코드

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

https://github.com/Waffle-Liu/STRODE

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STRODE: Stochastic Boundary Ordinary Differential Equation

https://github.com/Wangkkklll/UniPCGC

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UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach

https://github.com/Wei-Shao-Reg/GDR

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Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts

https://github.com/WenjieDu/PyGrinder

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PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/WenjieDu/PyPOTS

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Recurrent Neural Networks for Multivariate Time Series with Missing Values

https://github.com/WenjieDu/SAITS

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SAITS: Self-Attention-based Imputation for Time Series

https://github.com/WenjieDu/TSDB

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PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/Wensi-Tang/DPSN

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Interpretable Time-series Classification on Few-shot Samples

https://github.com/Wensi-Tang/OS-CNN

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Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

https://github.com/Wentao-Gao/TSI-forcasting

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TSI: A Multi-View Representation Learning Approach for Time Series Forecasting

https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries

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Unsupervised Scalable Representation Learning for Multivariate Time Series

https://github.com/Wickstrom/TimeSeriesXAI

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Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

https://github.com/WisdomShell/RewardAnything

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RewardAnything: Generalizable Principle-Following Reward Models

https://github.com/WuQiangXDU/Peri-midFormer

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Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

https://github.com/XN-BoWenMa/Chapter-category-identification

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Enhancing Identification of Structure Function of Academic Articles Using Contextual Information

https://github.com/XiaoxG/waveMotion-lightning

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Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem model

https://github.com/Xihaier/INR-TPC-Compression

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Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling

https://github.com/XiongxiaoXu/Mambaformer-in-Time-Series

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SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting

https://github.com/XiudingCai/MambaTS-pytorch

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MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting

https://github.com/YangLIN1997/AMLNet-DSAA2023

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AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting

https://github.com/YangLIN1997/Progressive-neural-network-for-multi-horizon-time-series-forecasting

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Progressive Neural Network for Multi-Horizon Time Series Forecasting

https://github.com/YangLIN1997/SSDNet-ICDM2021

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SSDNet: State Space Decomposition Neural Network for Time Series Forecasting

https://github.com/YangLiu9208/SAKDN

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Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition

https://github.com/Yitiann/MTST

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Multi-resolution Time-Series Transformer for Long-term Forecasting

https://github.com/Yruikk/MUSO

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MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes

https://github.com/Yu-Group/covid19-severity-prediction

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Curating a COVID-19 data repository and forecasting county-level death counts in the United States

https://github.com/Yu-Lingrui/DTAAD

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DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

https://github.com/YuanGongND/ast

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AST: Audio Spectrogram Transformer

https://github.com/YuchenJin/autolrs

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AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly

https://github.com/YukiBear426/AEFIN

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Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism

https://github.com/YukinoAsuna/SAMBA

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Simplified Mamba with Disentangled Dependency Encoding for Long-Term Time Series Forecasting

https://github.com/YuliaRubanova/latent_ode

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Latent ODEs for Irregularly-Sampled Time Series

https://github.com/Yunbo426/MIM

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Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

https://github.com/YuxiaoLuo0013/TFDNet

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TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting

https://github.com/Z-GCNETs/Z-GCNETs

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Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

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