Time Series Classification
243 papers with code • 51 benchmarks • 17 datasets
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
Benchmarks
These leaderboards are used to track progress in Time Series Classification
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Libraries
Use these libraries to find Time Series Classification models and implementationsDatasets
SHAPES
UCI Machine Learning Repository
PhysioNet Challenge 2012
US Economy
Most implemented papers
Bearing Fault Diagnosis Base on Multi-scale CNN and LSTM Model
Xiaohan-Chen/bear_fault_diagnosis
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journal 2020
Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis.
Neural Rough Differential Equations for Long Time Series
Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series.
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
huckiyang/Voice2Series-Reprogramming
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Learning to classify time series with limited data is a practical yet challenging problem.
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category.
Multi-Scale Convolutional Neural Networks for Time Series Classification
zdcuob/Fully-Convlutional-Neural-Networks-for-state-of-the-art-time-series-classification-
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These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance.
TimeNet: Pre-trained deep recurrent neural network for time series classification
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis
In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis.
Data augmentation using synthetic data for time series classification with deep residual networks
hfawaz/aaltd18
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This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted.
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
marccoru/elects
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In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.
