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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/ChenxiLiu-HNU/ST-LLM

공식 코드

Spatial-Temporal Large Language Model for Traffic Prediction

https://github.com/ChernovAndrey/chronos-forecasting-wasserstein

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Fine-Tuning a Time Series Foundation Model with Wasserstein Loss

https://github.com/ChineseBest/AT-504

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Apply Artificial Neural Network to Solving Manpower Scheduling Problem

https://github.com/ClaudiaShu/DeformTime

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DeformTime: Capturing Variable Dependencies with Deformable Attention for Time Series Forecasting

https://github.com/CompBioUIC/CoordinationModelSelectionFramework

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Framework for Inferring Following Strategies from Time Series of Movement Data

https://github.com/CompBioUIC/FLICA

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Coordination Event Detection and Initiator Identification in Time Series Data

https://github.com/D-ST-Sword/ATD

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Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method

https://github.com/DAMO-DI-ML/KDD2023-DCdetector

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DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

https://github.com/DENG-MIT/CRNN

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Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network

https://github.com/DENG-MIT/StiffNeuralODE

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Stiff Neural Ordinary Differential Equations

https://github.com/DIDNN/NPIs

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Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

https://github.com/DMIRLAB-Group/LSTD

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Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting

https://github.com/DSC-SPIDAL/rankpredictor

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Rank Position Forecasting in Car Racing

https://github.com/Daikon-Sun/AdjustAutocorrelation

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Adjusting for Autocorrelated Errors in Neural Networks for Time Series

https://github.com/Daniela-Shereen/GBRT-for-TSF

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Do We Really Need Deep Learning Models for Time Series Forecasting?

https://github.com/DarkEyes/VLTimeSeriesCausality

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Variable-lag Granger Causality for Time Series Analysis

https://github.com/DataMining-ClusteringAnalysis/CRAD-Clustering

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CRAD: Clustering with Robust Autocuts and Depth

https://github.com/DataScienceForPublicPolicy/hypML

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Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?

https://github.com/DatrikIntelligence/SoundnessXAI

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On the Soundness of XAI in Prognostics and Health Management (PHM)

https://github.com/DavidSabbagh/NeurIPS19_manifold-regression-meeg

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Manifold-regression to predict from MEG/EEG brain signals without source modeling

https://github.com/DavideBuffelli/TrASenD

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Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation

https://github.com/DehouZhang/Online-Search-with-Predictions

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Online Search With Best-Price and Query-Based Predictions

https://github.com/DentonJC/DIT

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DIT4BEARs Smart Roads Internship

https://github.com/DepartmentOfStatisticsPUE/rid-gig-economy-covid19

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COVID-19 and the gig economy in Poland

https://github.com/DiTEC-project/DiTEC_WDN_dataset

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DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks

https://github.com/DiffEqML/torchdyn

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On Neural Differential Equations

https://github.com/Dilfiraa/PS_Loss

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Patch-wise Structural Loss for Time Series Forecasting

https://github.com/Dntfreitas/GA_PSO_DEEP_LEARNING

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Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG

https://github.com/DominiqueMercier/PPML-TSA

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Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

https://github.com/DynamicsAndNeuralSystems/pinup_paper

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Parameter inference from a non-stationary unknown process

https://github.com/EVERGi/predict-then-schedule-university-activities

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Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs

https://github.com/EagleAdelaide/FinSen_Dataset

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Enhancing Financial Market Predictions: Causality-Driven Feature Selection

https://github.com/EdWangLoDaSc/DSOVT

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Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint

https://github.com/EduProgram/ePL-KRLS-FSM_plus

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Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach

https://github.com/EmergentSystemLabStudent/Prosodic-DAA

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Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

https://github.com/EmilienDupont/coin

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COIN: COmpression with Implicit Neural representations

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