Time Series Classification
242 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
Latent ODEs for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline
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We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
chickenbestlover/RNN-Time-series-Anomaly-Detection
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Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
LSTM Fully Convolutional Networks for Time Series Classification
We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
Multivariate LSTM-FCNs for Time Series Classification
houshd/MLSTM-FCN
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Over the past decade, multivariate time series classification has received great attention.
Deep learning for time series classification: a review
hfawaz/dl-4-tsc
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We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
angus924/rocket
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Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets.
A Transformer-based Framework for Multivariate Time Series Representation Learning
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
