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Time Series Classification

84 papers with code · Time Series

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

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Greatest papers with code

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

25 Oct 2016blue-yonder/tsfresh

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.

FEATURE IMPORTANCE FEATURE SELECTION TIME SERIES TIME SERIES CLASSIFICATION

sktime: A Unified Interface for Machine Learning with Time Series

17 Sep 2019alan-turing-institute/sktime

We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.

TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TIME SERIES FORECASTING

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

18 Nov 2019mlr-org/mlr

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.

TIME SERIES TIME SERIES CLASSIFICATION

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

NeurIPS 2019 Microsoft/EdgeML

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

TIME SERIES TIME SERIES CLASSIFICATION

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

NeurIPS 2018 Microsoft/EdgeML

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 SPEECH RECOGNITION TIME SERIES TIME SERIES CLASSIFICATION

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

NeurIPS 2018 Microsoft/EdgeML

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.

MULTIPLE INSTANCE LEARNING TIME SERIES TIME SERIES CLASSIFICATION

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

1 Jul 2016chickenbestlover/RNN-Time-series-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.

ANOMALY DETECTION OUTLIER DETECTION TIME SERIES TIME SERIES CLASSIFICATION

Deep learning for time series classification

1 Oct 2020hfawaz/dl-4-tsc

In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision.

ACTIVITY RECOGNITION DATA AUGMENTATION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION TRANSFER LEARNING

Deep learning for time series classification: a review

12 Sep 2018hfawaz/dl-4-tsc

We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.

TIME SERIES TIME SERIES CLASSIFICATION