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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/NivC/SINBAD

공식 코드

Set Features for Fine-grained Anomaly Detection

https://github.com/Nixtla/nixtla

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https://github.com/OctoberChang/klcpd_code

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Kernel Change-point Detection with Auxiliary Deep Generative Models

https://github.com/Olauwers/Applicability-and-interpretation-of-the-deterministic-weighted-cepstral-distance

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Applicability and interpretation of the deterministic weighted cepstral distance

https://github.com/Omkar-Ranadive/Earthquake-Detective

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Applying Machine Learning to Crowd-sourced Data from Earthquake Detective

https://github.com/OrigamiSL/TCCT2021

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TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting

https://github.com/PGelss/kFSA

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Feature space approximation for kernel-based supervised learning

https://github.com/PadraigC/kNNTutorial

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k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

https://github.com/PalaceTony/ProGen

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ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations

https://github.com/PatrickTUM/SEN12MS-CR-TS

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SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal

https://github.com/PatrikOkanovic/RS2

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Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning

https://github.com/Peng154/Delay-Embedding-based-Forecast-Machine

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DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation

https://github.com/PengleiGao/ETN-ODE

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Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction

https://github.com/PengleiGao/EgPDE-net

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EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables

https://github.com/PerdonLiu/CSE-Autoloss

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Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

https://github.com/PhIMaL/DeePyMoD

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DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/PneuC/Keiki

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Keiki: Towards Realistic Danmaku Generation via Sequential GANs

https://github.com/Priesemann-Group/mrestimator

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MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

https://github.com/QualityAssessment/DisCoVQA

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DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment

https://github.com/Quan-Zhou/Fairness-in-Learning-of-LDS

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Fairness in Forecasting of Observations of Linear Dynamical Systems

https://github.com/RRRussell/CauGNN/tree/main/MTS_TEGNN-master

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Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/Ranlot/single-parameter-fit

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Real numbers, data science and chaos: How to fit any dataset with a single parameter

https://github.com/RelationRx/SparseDDD

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Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data

https://github.com/RichardBean/IEEE-Predict-Optimize-Challenge

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Methodology for forecasting and optimization in IEEE-CIS 3rd Technical Challenge

https://github.com/RickardKarl/LearningUsingPrivilegedTimeSeries

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Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

https://github.com/RifleZhang/CORD_CPD

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Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks

https://github.com/RingBDStack/Fed_mood

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FedMood: Federated Learning on Mobile Health Data for Mood Detection

https://github.com/RoonakR/rational_model

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An attention model to analyse the risk of agitation and urinary tract infections in people with dementia

https://github.com/Roytsai27/Financial-GraphAttention

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FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

https://github.com/RudyMorel/scattering_covariance

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Scale Dependencies and Self-Similar Models with Wavelet Scattering Spectra

https://github.com/RuiZhang2016/Variational-Inference-for-SGP-Modulated-Hawkes-Process

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Variational Inference for Sparse Gaussian Process Modulated Hawkes Process

https://github.com/SIJIEJI/CLNet

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CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback

https://github.com/SalesforceAIResearch/uni2ts

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Unified Training of Universal Time Series Forecasting Transformers

https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset

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COVID-19 Data Analysis and Forecasting: Algeria and the World

https://github.com/SamuelWiqvist/PENs-and-ABC

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Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

https://github.com/SarveshVGharat/Plages-Identification

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An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar Observatory

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