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Time Series Classification

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하위 태스크: 2
평가표: 52
보존된 평가 기록: 231

Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.

Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/isaksamsten/castor

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Castor: Competing shapelets for fast and accurate time series classification

https://github.com/izotov93/nneten

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Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation

https://github.com/jaearly/miltimeseriesclassification

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Inherently Interpretable Time Series Classification via Multiple Instance Learning

https://github.com/jambo6/generalised-signature-method

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A Generalised Signature Method for Multivariate Time Series Feature Extraction

https://github.com/jambo6/neuralCDEs-via-logODEs

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Neural Rough Differential Equations for Long Time Series

https://github.com/jambo6/neuralRDEs

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Neural Rough Differential Equations for Long Time Series

https://github.com/janis-norden/Structural_Identifiability_Mapping

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On the importance of structural identifiability for machine learning with partially observed dynamical systems

https://github.com/jeongwhanchoi/LEAP

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Learnable Path in Neural Controlled Differential Equations

https://github.com/jhoelli/xtsc-bench

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XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification

https://github.com/jihyeonseong/SoM-TP

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Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings

https://github.com/jmoo2880/MPSTime.jl

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Using matrix-product states for time-series machine learning

https://github.com/jose-gilberto/leakysinelu

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Semi-Periodic Activation for Time Series Classification

https://github.com/jrb9/cosco

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COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification

https://github.com/jrzhang33/tce

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Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain

https://github.com/keebvin/iars

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Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution Selection

https://github.com/ketyi/Deep-2SRNN

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On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture Recognition

https://github.com/khairulislam/Timeseries-Explained

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WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models

https://github.com/langfengq/timemaster

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TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning

https://github.com/larsboecking/time_series_fingerprint

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Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets

https://github.com/lbzsteven/implet

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Implet: A Post-hoc Subsequence Explainer for Time Series Models

https://github.com/leezekun/vitst

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Time Series as Images: Vision Transformer for Irregularly Sampled Time Series

https://github.com/liuxz1011/todynet

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TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

https://github.com/lnthach/Mr-SEQL

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Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains

https://github.com/loadingbyte/timexplain

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timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers

https://github.com/lpphd/lightwaves

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Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS

https://github.com/lpphd/mtsscaling

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An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions

https://github.com/lsx-uniwue/susl4ts

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Semi-unsupervised Learning for Time Series Classification

https://github.com/lysarthas/scott

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Time Series Representation Learning with Supervised Contrastive Temporal Transformer

https://github.com/lzrvch/spikebench

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Spikebench: An open benchmark for spike train time-series classification

https://github.com/machine-discovery/deer

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Parallelizing non-linear sequential models over the sequence length

https://github.com/marccoru/crop-type-mapping

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Self-attention for raw optical Satellite Time Series Classification

https://github.com/marccoru/elects

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End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

https://github.com/mariorefoyo/multi-space

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Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification

https://github.com/mariusbock/context_har

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DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition

https://github.com/marthadais/aisclassification

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A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels

https://github.com/matsuo-shinnosuke/deep-attentive-time-warping

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Deep Attentive Time Warping

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