Time Series Classification
245 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
BorealTC
Most implemented papers
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.
Adversarial Attacks on Time Series
houshd/TS_Adv
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In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models.
Deep Neural Network Ensembles for Time Series Classification
Deep neural networks have revolutionized many fields such as computer vision and natural language processing.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
PV-Lab/AUTO-XRD
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npj Computational Materials 2019
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.
Trading via Image Classification
hardyqr/cnn-for-stock-market-prediction-pytorch
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The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis.
Set Functions for Time Series
BorgwardtLab/Set_Functions_for_Time_Series
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ICML 2020
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.
Self-attention for raw optical Satellite Time Series Classification
marccoru/crop-type-mapping
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The amount of available Earth observation data has increased dramatically in the recent years.
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
VSainteuf/pytorch-psetae
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CVPR 2020
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
Microsoft/EdgeML
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NeurIPS 2019
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
