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
BorealTC
Latest papers
TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault Diagnosis
Xiaohan-Chen/TFPred
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Control Engineering Practice 2024
Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the availability of massive labeled training data.
TSLANet: Rethinking Transformers for Time Series Representation Learning
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.
Swarm Characteristics Classification Using Neural Networks
This article presents a study on using supervised neural network time series classification (NN TSC) to predict key attributes and tactics of swarming autonomous agents for military contexts.
Proprioception Is All You Need: Terrain Classification for Boreal Forests
We show that the combination of two TC datasets yields a latent space that can be interpreted with the properties of the terrains.
Castor: Competing shapelets for fast and accurate time series classification
The transformation organizes shapelets into groups with varying dilation and allows the shapelets to compete over the time context to construct a diverse feature representation.
Robust Learning of Noisy Time Series Collections Using Stochastic Process Models with Motion Codes
For many applications, the data are mixed and consist of several types of noisy time series sequences modeled by multiple stochastic processes, making the forecasting and classification tasks even more challenging.
Class-incremental Learning for Time Series: Benchmark and Evaluation
zqiao11/tscil
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Real-world environments are inherently non-stationary, frequently introducing new classes over time.
ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection.
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
ericssonresearch/uda-4-tsc
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Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data.
Enhanced Spatio- Temporal Image Encoding for Online Human Activity Recognition
It can be done using 3D skeleton data extracted from a RGB+D camera.
