Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios

Jesse Piburn; Robert Stewart; April Morton

doi:10.1007/978-3-319-22786-3_18

Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios

Jesse Piburn, Robert Stewart, April Morton

Spatial Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Frequently questions we ask cannot be answered by simply looking at one indicator. To answer the question asking which countries are similar to one another economically over the past 20 years is not just a matter of looking at trends in gross domestic product (GDP) or unemployment rates; “economically” encompasses much more than just one or two measures. In this chapter, we propose a method called attribute portfolio distance (APD) and a variant trend only APD (TO-APD) to address questions such as these. APD/TO-APD is a spatiotemporal extension of a data-mining algorithm called dynamic time warping used to measure the similarity between two univariate time series.

Original language	English
Title of host publication	Advances in Geocomputation - Geocomputation 2015—The 13th International Conference
Editors	Daniel A. Griffith, Yongwan Chun, Denis J. Dean
Publisher	Springer Heidelberg
Pages	197-205
Number of pages	9
ISBN (Print)	9783319227856
DOIs	https://doi.org/10.1007/978-3-319-22786-3_18
State	Published - 2017
Event	13th International Conference on Advances in Geocomputation, Geocomputation 2015 - Dallas, United States Duration: May 20 2015 → May 23 2015

Publication series

Name	Advances in Geographic Information Science
ISSN (Print)	1867-2434
ISSN (Electronic)	1867-2442

Conference

Conference	13th International Conference on Advances in Geocomputation, Geocomputation 2015
Country/Territory	United States
City	Dallas
Period	05/20/15 → 05/23/15

Funding

This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

Dynamic time warping
High dimensional
Similarity
Spatiotemporal
Time series

Access to Document

10.1007/978-3-319-22786-3_18

Cite this

Piburn, J., Stewart, R., & Morton, A. (2017). Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios. In D. A. Griffith, Y. Chun, & D. J. Dean (Eds.), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference (pp. 197-205). (Advances in Geographic Information Science). Springer Heidelberg. https://doi.org/10.1007/978-3-319-22786-3_18

Piburn, Jesse ; Stewart, Robert ; Morton, April. / Attribute portfolio distance : A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios. Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. editor / Daniel A. Griffith ; Yongwan Chun ; Denis J. Dean. Springer Heidelberg, 2017. pp. 197-205 (Advances in Geographic Information Science).

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abstract = "Frequently questions we ask cannot be answered by simply looking at one indicator. To answer the question asking which countries are similar to one another economically over the past 20 years is not just a matter of looking at trends in gross domestic product (GDP) or unemployment rates; “economically” encompasses much more than just one or two measures. In this chapter, we propose a method called attribute portfolio distance (APD) and a variant trend only APD (TO-APD) to address questions such as these. APD/TO-APD is a spatiotemporal extension of a data-mining algorithm called dynamic time warping used to measure the similarity between two univariate time series.",

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Piburn, J, Stewart, R & Morton, A 2017, Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios. in DA Griffith, Y Chun & DJ Dean (eds), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Advances in Geographic Information Science, Springer Heidelberg, pp. 197-205, 13th International Conference on Advances in Geocomputation, Geocomputation 2015, Dallas, United States, 05/20/15. https://doi.org/10.1007/978-3-319-22786-3_18

Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios. / Piburn, Jesse; Stewart, Robert; Morton, April.
Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. ed. / Daniel A. Griffith; Yongwan Chun; Denis J. Dean. Springer Heidelberg, 2017. p. 197-205 (Advances in Geographic Information Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Frequently questions we ask cannot be answered by simply looking at one indicator. To answer the question asking which countries are similar to one another economically over the past 20 years is not just a matter of looking at trends in gross domestic product (GDP) or unemployment rates; “economically” encompasses much more than just one or two measures. In this chapter, we propose a method called attribute portfolio distance (APD) and a variant trend only APD (TO-APD) to address questions such as these. APD/TO-APD is a spatiotemporal extension of a data-mining algorithm called dynamic time warping used to measure the similarity between two univariate time series.

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Piburn J, Stewart R, Morton A. Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios. In Griffith DA, Chun Y, Dean DJ, editors, Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Springer Heidelberg. 2017. p. 197-205. (Advances in Geographic Information Science). doi: 10.1007/978-3-319-22786-3_18

Attribute portfolio distance: A dynamic time warping-based approach to comparing and detecting common spatiotemporal patterns among multiattribute data portfolios

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