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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/news-vt/makassar-ml

공식 코드

A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities

https://github.com/ngruver/llmtime

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Large Language Models Are Zero-Shot Time Series Forecasters

https://github.com/ngunnar/latent-linear-dynamics-in-spatiotemporal-medical-data

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Unsupervised dynamic modeling of medical image transformation

https://github.com/ngunnar/med-dyn-reg

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Unsupervised dynamic modeling of medical image transformation

https://github.com/nhatthanhtran/fwin2023

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Fourier-Mixed Window Attention: Accelerating Informer for Long Sequence Time-Series Forecasting

https://github.com/nhwerge/AdaVol

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AdaVol: An Adaptive Recursive Volatility Prediction Method

https://github.com/nicholasjohnson2020/discretecompressedsensing.jl

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Compressed Sensing: A Discrete Optimization Approach

https://github.com/nicholasjohnson2020/sparselowranksoftware

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Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

https://github.com/nicocourts/bundle-networks

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Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps

https://github.com/nicolopinci/deepgravilens

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DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data

https://github.com/nikitazozoulenko/kernel-timeseries-anomaly-detection

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Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

https://github.com/nikolajthams/its-time

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Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past

https://github.com/nitzanlab/phase2vec

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Phase2vec: Dynamical systems embedding with a physics-informed convolutional network

https://github.com/nixtla/hierarchicalforecast

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HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python

https://github.com/nla-group/fabba

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An efficient aggregation method for the symbolic representation of temporal data

https://github.com/nmheim/torsk

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Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network

https://github.com/nnaisense/bayesian-flow-networks

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Bayesian Flow Networks

https://github.com/nnzhan/MTGNN

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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

https://github.com/noelkelias/multitok

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MultiTok: Variable-Length Tokenization for Efficient LLMs Adapted from LZW Compression

https://github.com/nrel/phase-space-sampling

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Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

https://github.com/numenta/NAB

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Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

https://github.com/nus-hpc-ai-lab/dd-ranking

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DD-Ranking: Rethinking the Evaluation of Dataset Distillation

https://github.com/nvecoven/BRC

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A bio-inspired bistable recurrent cell allows for long-lasting memory

https://github.com/nx-ai/tirex

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TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning

https://github.com/nyuolab/gateformer

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Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations

https://github.com/nzl5116190/basisformer

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BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis

https://github.com/oetu/otis

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Towards Generalisable Time Series Understanding Across Domains

https://github.com/oezyurty/cluda

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Contrastive Learning for Unsupervised Domain Adaptation of Time Series

https://github.com/olivercliff/pyspi

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Unifying Pairwise Interactions in Complex Dynamics

https://github.com/oliverobst/decorating

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The Power of Linear Recurrent Neural Networks

https://github.com/olivesgatech/Elastic-Impedance-Inversion-Using-Recurrent-Neural-Networks

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Semi-supervised Sequence Modeling for Elastic Impedance Inversion

https://github.com/olivesgatech/olives_dataset

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OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

https://github.com/olliestephenson/dpm-rnn-public

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Deep Learning-based Damage Mapping with InSAR Coherence Time Series

https://github.com/olspert/BGLST

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Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend

https://github.com/omarbahri/sets

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Shapelet-Based Counterfactual Explanations for Multivariate Time Series

https://github.com/omitakahiro/NeuralNetworkPointProcess

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Fully Neural Network based Model for General Temporal Point Processes

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