Time Series Classification
238 papers with code • 50 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
Libraries
Use these libraries to find Time Series Classification models and implementationsDatasets
Most implemented papers
Imaging Time-Series to Improve Classification and Imputation
We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.
Precision and Recall for Time Series
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time.
Proximity Forest: An effective and scalable distance-based classifier for time series
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.
Insights into LSTM Fully Convolutional Networks for Time Series Classification
In this paper, we perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-module.
The Signature Kernel is the solution of a Goursat PDE
Recently, there has been an increased interest in the development of kernel methods for learning with sequential data.
Distributed and parallel time series feature extraction for industrial big data applications
This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.
Spikebench: An open benchmark for spike train time-series classification
Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks.
catch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
Time-Series Event Prediction with Evolutionary State Graph
In this paper, we present evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time.
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series.