Timeseries Classification Task

A Timeseries Classification Task is a sequence classification task for timeseries data.

• Context:
  • It can be solved by a Timeseries Classification System (that implements a timeseries classification algorithm).
  • …
• Example(s):
  • Anguita et al's Human Activity Recognition Using Smartphones Task [1].
  • EEG Signal Classification.
  • …
• Counter-Example(s):
  • Text Classification Task.
• See: Image Classification.

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References

2020

• (Dempster et al., 2020) ⇒ Angus Dempster, François Petitjean, and Geoffrey I. Webb. (2020). “ROCKET: Exceptionally Fast and Accurate Time Series Classification Using Random Convolutional Kernels.” In: Data Mining and Knowledge Discovery, 34(5).

2017

• https://burakhimmetoglu.com/2017/08/22/time-series-classification-with-tensorflow/
  • QUOTE: Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. classification of EEG signals), then possible features would involve power spectra at various frequency bands, Hjorth parameters and several other specialized statistical properties.

2012

• https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
  • QUOTE: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. ...

    ... The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
    The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

2001

• (Jtastny et al., 2001) ⇒ J. Stastny., Pavel Sovka, and A. Stancak. (2001). “EEG Signal Classification.” In: Engineering in Medicine and Biology Society,

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