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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/bornabr/rscnet

공식 코드

RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

https://github.com/boschresearch/NeuTraL-AD

공식 코드

Neural Transformation Learning for Deep Anomaly Detection Beyond Images

https://github.com/boschresearch/continuous-recurrent-units

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Modeling Irregular Time Series with Continuous Recurrent Units

https://github.com/boschresearch/expclr

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Utilizing Expert Features for Contrastive Learning of Time-Series Representations

https://github.com/boschresearch/local_neural_transformations

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Detecting Anomalies within Time Series using Local Neural Transformations

https://github.com/bperezorozco/ordinal_tsf

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MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

https://github.com/brechtlaperre/DTW_measure

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Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning

https://github.com/brinkl/graphhetnet

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Few-shot human motion prediction for heterogeneous sensors

https://github.com/bruno686/dcf

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Double Correction Framework for Denoising Recommendation

https://github.com/bycnfz/weather2k

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Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations

https://github.com/bytedance/pargo

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MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

https://github.com/c-lyu/traffic4cast2022-tse

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Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting

https://github.com/c-quilo/adversarial-AE-LSTM

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Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

https://github.com/cagatayyildiz/ODE2VAE

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ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks

https://github.com/cambridge-mlg/convcnp

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Convolutional Conditional Neural Processes

https://github.com/cambridge-mlg/recombiner

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RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations

https://github.com/cameron-chen/mgm

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Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models

https://github.com/capsar/the-rise-of-diffusion-models-in-time-series-forecasting

공식 코드

The Rise of Diffusion Models in Time-Series Forecasting

https://github.com/capsar/ts-inverse

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TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models

https://github.com/capwidow/w-transformer

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W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting

https://github.com/carlosjva/pad

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A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems

https://github.com/carlosloza/DNDBN

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Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling

https://github.com/carrtesy/m2n2

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When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

https://github.com/castacks/cvar-energy-risk-deep-model

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CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model

https://github.com/catherinehao/timetuner

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TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations

https://github.com/catniplab/svmc

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Streaming Variational Monte Carlo

https://github.com/catniplab/vjf

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Variational online learning of neural dynamics

https://github.com/cauchyturing/Deconv_SAX

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Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

https://github.com/cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

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Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

https://github.com/causalens/cdml-neurips2020

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Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data

https://github.com/ccaribe9/adaptstdepf

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An adaptive standardisation methodology for Day-Ahead electricity price forecasting

https://github.com/cchallu/dghl

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Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection

https://github.com/cchallu/n-hits

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N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

https://github.com/cchallu/nbeatsx

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Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

https://github.com/cchallu/spectranet

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SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data

https://github.com/cdjellen/otbench

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Effective Benchmarks for Optical Turbulence Modeling

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