TY - JOUR
T1 - A Survey on Dimensionality Reduction Techniques for Time-Series Data
AU - Ashraf, Mohsena
AU - Anowar, Farzana
AU - Setu, Jahanggir H.
AU - Chowdhury, Atiqul I.
AU - Ahmed, Eshtiak
AU - Islam, Ashraful
AU - Al-Mamun, Abdullah
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Time-series data
KW - dimensionality reduction
KW - high-dimensional data
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85159684478&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159684478&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3269693
DO - 10.1109/ACCESS.2023.3269693
M3 - Article
AN - SCOPUS:85159684478
SN - 2169-3536
VL - 11
SP - 42909
EP - 42923
JO - IEEE Access
JF - IEEE Access
ER -