Temporal kernel descriptors for learning with time-sensitive patterns

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 ful...

Full description

Saved in:
Bibliographic Details
Main Authors: SAHOO, Doyen, SHARMA, Abhishek, HOI, Steven C. H., ZHAO, Peilin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/3409
https://ink.library.smu.edu.sg/context/sis_research/article/4410/viewcontent/Temporalkerneldescriptorsforlearningwithtime_sensitivepatterns.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-4410
record_format dspace
spelling sg-smu-ink.sis_research-44102018-12-07T08:16:12Z Temporal kernel descriptors for learning with time-sensitive patterns SAHOO, Doyen SHARMA, Abhishek HOI, Steven C. H., ZHAO, Peilin 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. 2016-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3409 info:doi/10.1137/1.9781611974348.61 https://ink.library.smu.edu.sg/context/sis_research/article/4410/viewcontent/Temporalkerneldescriptorsforlearningwithtime_sensitivepatterns.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Computer Sciences
Databases and Information Systems
Numerical Analysis and Scientific Computing
SAHOO, Doyen
SHARMA, Abhishek
HOI, Steven C. H.,
ZHAO, Peilin
Temporal kernel descriptors for learning with time-sensitive patterns
description 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.
format text
author SAHOO, Doyen
SHARMA, Abhishek
HOI, Steven C. H.,
ZHAO, Peilin
author_facet SAHOO, Doyen
SHARMA, Abhishek
HOI, Steven C. H.,
ZHAO, Peilin
author_sort SAHOO, Doyen
title Temporal kernel descriptors for learning with time-sensitive patterns
title_short Temporal kernel descriptors for learning with time-sensitive patterns
title_full Temporal kernel descriptors for learning with time-sensitive patterns
title_fullStr Temporal kernel descriptors for learning with time-sensitive patterns
title_full_unstemmed Temporal kernel descriptors for learning with time-sensitive patterns
title_sort temporal kernel descriptors for learning with time-sensitive patterns
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3409
https://ink.library.smu.edu.sg/context/sis_research/article/4410/viewcontent/Temporalkerneldescriptorsforlearningwithtime_sensitivepatterns.pdf
_version_ 1770573162818306048