
Farzana Anowar, PhD
- Collaborator, HCI Wing
Senior Data Analytics Specialist
+1 more affiliationGovernment of Saskatchewan, Canada
Research Interests
Machine Learning, Deep Learning, Data Science, Artificial Intelligence, Pattern Recognition
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
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.

Senior Data Analytics Specialist
+1 more affiliationGovernment of Saskatchewan, Canada
Machine Learning, Deep Learning, Data Science, Artificial Intelligence, Pattern Recognition

Doctoral Researcher
Gamification Group
Tampere University, Finland
Human-Computer Interaction, Human-Robot Interaction, Gamification and Assistive Technologies

Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact