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
Latest papers with no code
Advancing Time Series Classification with Multimodal Language Modeling
In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm.
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively).
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings
In this paper, we propose a novel temporal pooling method with diverse perspective learning: Selection over Multiple Temporal Poolings (SoM-TP).
Dataset Condensation for Time Series Classification via Dual Domain Matching
Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains.
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering
Time series classification is of significant importance in monitoring structural systems.
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing.
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
The core component of Log-NCDEs is the Log-ODE method, a tool from the study of rough paths for approximating a CDE's solution.
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification
Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution.
SMORE: Similarity-based Hyperdimensional Domain Adaptation for Multi-Sensor Time Series Classification
Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors.
