Time Series Classification

121 papers with code • 29 benchmarks • 5 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

Greatest papers with code

Distributed and parallel time series feature extraction for industrial big data applications

blue-yonder/tsfresh 25 Oct 2016

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.

Classification Feature Importance +4

Benchmarking time series classification -- Functional data vs machine learning approaches

mlr-org/mlr 18 Nov 2019

In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.

Additive models Classification +3

Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

Microsoft/EdgeML NeurIPS 2019

The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.

Classification General Classification +2

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

Microsoft/EdgeML NeurIPS 2018

FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.

Action Classification Language Modelling +3

Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices

Microsoft/EdgeML NeurIPS 2018

We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction.

Classification General Classification +3

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

timeseriesAI/tsai 16 Dec 2020

ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier.

Classification General Classification +2

XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

timeseriesAI/tsai 10 Sep 2020

Thus, XCM architecture enables a good generalization ability on both small and large datasets, while allowing the full exploitation of a faithful post-hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions.

General Classification Time Series +1

Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

timeseriesAI/tsai 24 Feb 2020

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series.

Classification General Classification +2

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

timeseriesAI/tsai 29 Oct 2019

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.

Classification General Classification +2