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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/bartbussmann/NAVAR

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Neural Additive Vector Autoregression Models for Causal Discovery in Time Series

https://github.com/bayesflow-org/jana-paper

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JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models

https://github.com/bayesflow-org/neural-superstatistics

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Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models

https://github.com/bcsaldias/yes-no-crowdsourcing

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A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

https://github.com/bee-hive/MedGP

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Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction

https://github.com/beginner-sketch/gmrl

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Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

https://github.com/ben-hayes/sinusoidal-gradient-descent

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Sinusoidal Frequency Estimation by Gradient Descent

https://github.com/benedekrozemberczki/pytorch_geometric_temporal

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Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

https://github.com/benjaminfiltjens/ms-gcn

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Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks

https://github.com/benjaminmlucas/sourcerer

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A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping

https://github.com/bennytmt/llmsfortimeseries

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Are Language Models Actually Useful for Time Series Forecasting?

https://github.com/bennytmt/ts_models

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Are Language Models Actually Useful for Time Series Forecasting?

https://github.com/berserkhmdvhb/THESIS-MS-Asset-Price-Forecast

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Asset Price Forecasting using Recurrent Neural Networks

https://github.com/bgs25/OrderRegression

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High-dimensional regression with potential prior information on variable importance

https://github.com/bgu-cs-vil/timepoint

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TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning

https://github.com/bharathsudharsan/cnn_on_mcu

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Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware

https://github.com/biboamy/FBA-Fall19

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Score-informed Networks for Music Performance Assessment

https://github.com/biozoo/MDR_S-map

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Reconstructing large networks with time-varying interactions

https://github.com/bit1029public/ADRQN

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On Improving Deep Reinforcement Learning for POMDPs

https://github.com/bjmch/dl-radiotransient

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Deep learning-based deconvolution for interferometric radio transient reconstruction

https://github.com/bking124/warpDLM-reproducible-code

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Warped Dynamic Linear Models for Time Series of Counts

https://github.com/blacksnail789521/LLM4TS

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LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

https://github.com/blacksnail789521/timedrl

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TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

https://github.com/blue-yonder/tsfresh

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Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/bnl-daq-ldrd/neuralcompression_v2

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Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

https://github.com/bnusss/GGN

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A General Deep Learning Framework for Network Reconstruction and Dynamics Learning

https://github.com/bobzwr/cross-reconstruction-transformer

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Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer

https://github.com/bojesomo/Traffic4Cast2021-SwinUNet3D

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SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers

https://github.com/bombooooo/cnts

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CNTS: Cooperative Network for Time Series

https://github.com/bonaldli/spatiotemporal-ts-representation-learning

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Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping

https://github.com/boniolp/dCAM

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dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification

https://github.com/boniolp/kGraph

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$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering

https://github.com/borealisai/continuous-latent-process-flows

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Continuous Latent Process Flows

https://github.com/borealisai/scaleformer

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Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

https://github.com/boreshkinai/fc-gaga

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FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting

https://github.com/boreshkinai/nbeats-midterm

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N-BEATS neural network for mid-term electricity load forecasting

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