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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/houshd/LSTM-FCN-Ablation

공식 코드

Insights into LSTM Fully Convolutional Networks for Time Series Classification

https://github.com/houshd/MLSTM-FCN

공식 코드

Multivariate LSTM-FCNs for Time Series Classification

https://github.com/houshd/TS_Adv

공식 코드

Adversarial Attacks on Time Series

https://github.com/houtan-ghaffari/SSCCSLR

공식 코드

An Efficient Method for the Classification of Croplands in Scarce-Label Regions

https://github.com/houyikai/ldm-logsparse-decomposable-multiscaling

공식 코드

Breaking the Context Bottleneck on Long Time Series Forecasting

https://github.com/howard-hou/rwkv-ts

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RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks

https://github.com/hse-lambda/light_curve_approx

공식 코드

Understanding of the properties of neural network approaches for transient light curve approximations

https://github.com/hsteude/concept-learning-for-cps

공식 코드

Learning Physical Concepts in Cyber-Physical Systems: A Case Study

https://github.com/hsy23/kdd23_dyneformer

공식 코드

One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms

https://github.com/hturbe/interprettime

공식 코드

Evaluation of post-hoc interpretability methods in time-series classification

https://github.com/huangst21/timekan

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TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting

https://github.com/huankoh/cst-gl

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Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

https://github.com/huaxiuyao/DMVST-Net

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Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

https://github.com/huckiyang/Voice2Series-Reprogramming

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Voice2Series: Reprogramming Acoustic Models for Time Series Classification

https://github.com/hudsonchen/MMDF_cubature

공식 코드

Stationary MMD Points for Cubature

https://github.com/huggingface/datasets

공식 코드

Datasets: A Community Library for Natural Language Processing

https://github.com/hugopstackhouse/MPSTime.jl

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Using matrix-product states for time-series machine learning

https://github.com/huihevv/fairfor

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Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

https://github.com/hulei7/replaycad

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ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection

https://github.com/hulkima/pdrec

공식 코드

Plug-in Diffusion Model for Sequential Recommendation

https://github.com/humaticslab/gtm-transformer

공식 코드

Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends

https://github.com/humaticslab/pop-mining-potential-performance

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POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion

https://github.com/hundredl/mg-tsd

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MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process

https://github.com/hyang0129/ngafiddataset

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A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID

https://github.com/hyren/acl

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Adversarial Constraint Learning for Structured Prediction

https://github.com/hysonlab/graphlasso

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Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

https://github.com/hysynth/hysynthparametric

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Synthesis of Parametric Hybrid Automata from Time Series

https://github.com/hyunwookl/tilde-q

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TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

https://github.com/i6092467/GVAR

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Interpretable Models for Granger Causality Using Self-explaining Neural Networks

https://github.com/iDEA-iSAIL-Lab-UIUC/TaTS

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Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative

https://github.com/iamhuijben/som-cpc

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SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series

https://github.com/iastro-pt/gpyrn

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Modelling stellar activity with Gaussian process regression networks

https://github.com/ibm-granite/granite-tsfm

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Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

https://github.com/ibm/afdistill

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AlphaFold Distillation for Protein Design

https://github.com/ibm/multi-variate-parallel-transformer

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A foundation model with multi-variate parallel attention to generate neuronal activity

https://github.com/ibm/spatial-lstm

공식 코드

A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

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