Journal Article2023Cited by 48

A Survey on Dimensionality Reduction Techniques for Time-series Data

Mohsena Ashraf, Farzana Anowar, Jahanggir H Setu, Atiqul I Chowdhury, Eshtiak Ahmed, Ashraful Islam, Abdullah Al-Mamun

IEEE Access

IEEE, Vol. 11, pp. 42909-42923, ISBN: 2169-3536

DOI: 10.1109/access.2023.3269693Open AccessURL

Abstract

Data analysis in modern times involves working with large volumes of data, including time-series data. This type of data is characterized by its high dimensionality, enormous volume, and the presence of both noise and redundant features. However, the "curse of dimensionality" often causes issues for learning approaches, which can fail to capture the temporal dependencies present in time-series data. To address this problem, it is essential to reduce dimensionality while preserving the intrinsic properties of temporal dependencies. This will help to avoid lower learning and predictive performances. This study presents twelve different dimensionality reduction algorithms that are specifically suited for working with time-series data and fall into different categories, such as supervision, linearity, time and memory complexity, hyper-parameters, and drawbacks.

Keywords

  • Dimensionality reduction
  • Time series
  • temporal dependencies
  • Curse of dimensionality
  • linear dimensionality reduction
  • supervised dimensionality reduction
  • time series clustering
  • feature redundancy

CCDS Authors

Farzana Anowar, PhD
Farzana Anowar, PhD

Farzana Anowar, PhD

  • Collaborator, HCI Wing

  • Senior Data Analytics Specialist

    Government of Saskatchewan, Canada

  • PhD

    University of Regina

Published at

HCII, IEEE Access

Eshtiak Ahmed
Eshtiak Ahmed

Eshtiak Ahmed

  • Collaborator, HCI Wing

  • Doctoral Researcher

    Gamification Group

    Tampere University, Finland

Published at

UbiComp, ICIP, HCII, IEEE Access

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

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