Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2016
Abstract
Detecting temporal patterns is one of the most prevalent challenges while mining data. Often, timestamps or information about when certain instances or events occurred can provide us with critical information to recognize temporal patterns. Unfortunately, most existing techniques are not able to fully extract useful temporal information based on the time (especially at different resolutions of time). They miss out on 3 crucial factors: (i) they do not distinguish between timestamp features (which have cyclical or periodic properties) and ordinary features; (ii) they are not able to detect patterns exhibited at different resolutions of time (e.g. different patterns at the annual level, and at the monthly level);and (iii) they are not able to relate different features (e.g. multimodal features) of instances with different temporal properties (e.g. while predicting stock prices, stock fundamentals may have annual patterns, and at the same time factors like peer stock prices and global markets may exhibit daily patterns). To solve these issues, we offer a novel multiple-kernel learning view and develop Temporal Kernel Descriptors which utilize Kernel functions to comprehensively detect temporal patterns by deriving relationship of instances with the time features. We automatically learn the optimal kernel function, and hence the optimal temporal similarity between two instances. We formulate the optimization as a Multiple Kernel Learning (MKL) problem. We empirically evaluate its performance by solving the optimization using Online MKL.
Discipline
Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2016 SIAM International Conference on Data Mining: May 5-7, Miami, Florida, USA
First Page
540
Last Page
548
ISBN
9781611974348
Identifier
10.1137/1.9781611974348.61
Publisher
SIAM
City or Country
Philadelphia, PA
Citation
SAHOO, Doyen; SHARMA, Abhishek; HOI, Steven C. H.; and ZHAO, Peilin.
Temporal kernel descriptors for learning with time-sensitive patterns. (2016). Proceedings of the 2016 SIAM International Conference on Data Mining: May 5-7, Miami, Florida, USA. 540-548.
Available at: https://ink.library.smu.edu.sg/sis_research/3409
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1137/1.9781611974348.61
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons