CITATION.cff

# YAML 1.2
---
abstract: "The goal of mcfly is to ease the use of deep learning technology for time series classification. The advantage of deep learning is that it can handle raw data directly, without the need to compute signal features. Deep learning does not require expert domain knowledge about the data, and has been shown to be competitive with conventional machine learning techniques. As an example, you can apply mcfly on accelerometer data for activity classification."
authors:
  -
    affiliation: "Netherlands eScience Center"
    family-names: Kuppevelt
    given-names: Dafne
    name-particle: van
  -
    affiliation: "Netherlands eScience Center"
    family-names: Meijer
    given-names: Christiaan
  -
    affiliation: "Netherlands eScience Center"
    family-names: Hees
    given-names: Vincent
    name-particle: van
    orcid: "https://orcid.org/0000-0003-0182-9008"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Bos
    given-names: Patrick
  -
    affiliation: "Netherlands eScience Center"
    family-names: Spaaks
    given-names: Jurriaan
  -
    affiliation: "Netherlands eScience Center"
    family-names: Kuzak
    given-names: Mateusz
    orcid: "https://orcid.org/0000-0003-0087-6021"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Huber
    given-names: Florian
    orcid: "https://orcid.org/0000-0002-3535-9406"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Hidding
    given-names: Johan
  -
    affiliation: "Netherlands eScience Center"
    family-names: Ploeg
    given-names: Atze
    name-particle: "van der"
cff-version: "1.0.3"
date-released: 2018-01-16
doi: "10.5281/zenodo.596127"
keywords:
  - "machine learning"
  - "deep learning"
  - "time series"
  - "automated machine learning"
license: "Apache-2.0"
message: "If you use this software, please cite it using these metadata."
title: "mcfly: deep learning for time series"
version: "1.0.4"
...