Topological Time-series Classification
| dc.contributor.advisor | Chapman, David | |
| dc.contributor.author | Collins, Joseph Robert | |
| dc.contributor.department | Computer Science and Electrical Engineering | |
| dc.contributor.program | Computer Science | |
| dc.date.accessioned | 2022-09-29T15:37:57Z | |
| dc.date.available | 2022-09-29T15:37:57Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | We establish a strong connection between Topological Data Analysis (TDA)and the field of time-series classification. This is accomplished via two novel contri- butions. The first is a method for extracting topological information from a time series in a manner which circumvents the expensive computation normally required. The second is an adaption of an existing topological summary to create an efficient topological time-series pooling operator. Taken together, these insights enable a new classifier, TopRocket (Topological Random Convolutional Kernel Transform). Using standard comparison methods, TopRocket ranks second among all univariate time-series classifiers. Further, TopRocket is the best performing scalable classifier. As a final contribution, we provide the fastest available implementation of the algo- rithm for computing time-series sublevel set persistence. This provides a foundation for further investigation of TDA for time-series classification. This work lies at the intersection of TDA and machine learning, where suc- cess is dependent on efficiently extracting topological information from data and using that information with downstream models. For this purpose, we develop an efficient, data-based, topological summary, the Betti pooling operator. We rigor- ously demonstrate the utility of TopRocket by evaluating its performance against the University of California Riverside (UCR) Time Series Archive, a widely used benchmark collection of data sets. TopRocket is the first TDA-based classifier to obtain state-of-the art results on any similarly competitive problem. We strongly believe that TDA will play a central role in the future of machine learning. | |
| dc.format | application:pdf | |
| dc.genre | dissertations | |
| dc.identifier | doi:10.13016/m25wql-rire | |
| dc.identifier.other | 12533 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25985 | |
| dc.language | en | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Theses and Dissertations Collection | |
| dc.relation.ispartof | UMBC Graduate School Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu | |
| dc.source | Original File Name: Collins_umbc_0434D_12533.pdf | |
| dc.subject | Machine Learning | |
| dc.subject | Time-series Classification | |
| dc.subject | Topological Data Analysis | |
| dc.title | Topological Time-series Classification | |
| dc.type | Text | |
| dcterms.accessRights | Distribution Rights granted to UMBC by the author. | |
| dcterms.accessRights | Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission. |