A Survey on Dimensionality Reduction Techniques for Time-Series Data
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” o...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10107391/ |
| _version_ | 1827904510454923264 |
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| author | Mohsena Ashraf Farzana Anowar Jahanggir H. Setu Atiqul I. Chowdhury Eshtiak Ahmed Ashraful Islam Abdullah Al-Mamun |
| author_facet | Mohsena Ashraf Farzana Anowar Jahanggir H. Setu Atiqul I. Chowdhury Eshtiak Ahmed Ashraful Islam Abdullah Al-Mamun |
| author_sort | Mohsena Ashraf |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T00:28:23Z |
| format | Article |
| id | doaj.art-c3fa3edc011d44f9adf2d1c64331ad22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:28:23Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c3fa3edc011d44f9adf2d1c64331ad222023-07-10T23:00:42ZengIEEEIEEE Access2169-35362023-01-0111429094292310.1109/ACCESS.2023.326969310107391A Survey on Dimensionality Reduction Techniques for Time-Series DataMohsena Ashraf0Farzana Anowar1https://orcid.org/0000-0002-1535-7323Jahanggir H. Setu2Atiqul I. Chowdhury3Eshtiak Ahmed4Ashraful Islam5https://orcid.org/0000-0003-2367-2013Abdullah Al-Mamun6Department of Computer Science, University of Colorado Boulder, Boulder, CO, USADepartment of Computer Science, University of Regina, Regina, SK, CanadaDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, United International University, Dhaka, BangladeshFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandCenter for Computational and Data Sciences (CCDS), Independent University, Bangladesh, Dhaka, BangladeshSchool of Computer and Cyber Sciences, Augusta University, Augusta, GA, USAData 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.https://ieeexplore.ieee.org/document/10107391/Time-series datadimensionality reductionhigh-dimensional datamachine learning |
| spellingShingle | Mohsena Ashraf Farzana Anowar Jahanggir H. Setu Atiqul I. Chowdhury Eshtiak Ahmed Ashraful Islam Abdullah Al-Mamun A Survey on Dimensionality Reduction Techniques for Time-Series Data IEEE Access Time-series data dimensionality reduction high-dimensional data machine learning |
| title | A Survey on Dimensionality Reduction Techniques for Time-Series Data |
| title_full | A Survey on Dimensionality Reduction Techniques for Time-Series Data |
| title_fullStr | A Survey on Dimensionality Reduction Techniques for Time-Series Data |
| title_full_unstemmed | A Survey on Dimensionality Reduction Techniques for Time-Series Data |
| title_short | A Survey on Dimensionality Reduction Techniques for Time-Series Data |
| title_sort | survey on dimensionality reduction techniques for time series data |
| topic | Time-series data dimensionality reduction high-dimensional data machine learning |
| url | https://ieeexplore.ieee.org/document/10107391/ |
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