# Time Series Classification

233 papers with code • 39 benchmarks • 14 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 implementations## Datasets

## 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

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

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

Over the past decade, multivariate time series classification has received great attention.

# Deep learning for time series classification: a review

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

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.