Self-organizing time map: An abstraction of temporal multivariate patterns

Sarlin, Peter

doi:10.1016/j.neucom.2012.07.011

Cited by 43 publications

(30 citation statements)

References 40 publications

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“…Additionally, the integration of customer segmentation, campaign response and segment migration modeling provides decision makers with an effective analytical CRM for better campaign management. Future work should focus on addressing the changing nature of customer segments by attempting visual temporal clustering, something that the Self-Organizing Time Map (Sarlin, 2011) holds promise for.…”

Section: Resultsmentioning

confidence: 99%

“…While the SOM is an ideal tool for data and dimensionality reduction, identifying temporal movements in a SOM model is not a simple process (see Sarlin (2011) for a review of time in SOMs). Previously, trajectories have been a common means to illustrate temporal movements of individual data records on the SOM grid (see e.g., Eklund, Back, Vanharanta and Visa, 2003;Sarlin and Marghescu, 2011).…”

Section: Transition Probabilities On the Sommentioning

confidence: 99%

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Combining visual customer segmentation and response modeling

Yao

Sarlin

Eklund

et al. 2013

Neural Comput & Applic

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Section: Resultsmentioning

confidence: 99%

Section: Transition Probabilities On the Sommentioning

confidence: 99%

Combining visual customer segmentation and response modeling

Yao

Sarlin

Eklund

et al. 2013

Neural Comput & Applic

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“…The recently introduced Self-Organizing Time Map (SOTM) (Sarlin, 2013b) is unique in that it provides means for visualizing how cross-sectional structures (i.e., clusters) evolve over time. Although the only difference to the above approach is the addition of a time dimension to the mapping, it provides an approach that truly represents the three dimensions of the data cube.…”

Section: Financial Stability Map Over Timementioning

confidence: 99%

“…The SOTM (Sarlin, 2013b) uses the clustering and projection capabilities of the standard SOM for visualization and abstraction of temporal structural changes in data. Here, t (where t =…”

Section: Visrisk: a Visualization Platform For Macroprudential Analysismentioning

confidence: 99%

Macroprudential oversight, risk communication and visualization

Sarlin

2016

Journal of Financial Stability

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This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any organization, broad and effective external communication of timely information related to systemic risks is a key mandate of macroprudential supervisors, further stressing the importance of simple representations of complex data. This paper focuses on the background and theory of information visualization and visual analytics, as well as techniques within these fields, as potential means for risk communication. We define the task of visualization in risk communication, discuss the structure of macroprudential data, and review visualization techniques applied to systemic risk. We conclude that two essential, yet rare, features for supporting the analysis of big data and communication of risks are analytical visualizations and interactive interfaces. For visualizing the so-called macroprudential data cube, we provide the VisRisk platform with three modules: plots, maps and networks. While VisRisk is herein illustrated with five web-based interactive visualizations of systemic risk indicators and models, the platform enables and is open to the visualization of any data from the macroprudential data cube. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any organization, broad and effective external communication of timely information related to systemic risks is a key mandate of macroprudential supervisors, further stressing the importance of simple representations of complex data. This paper focuses on the background and theory of information visualization and visual analytics, as well as techniques within these fields, as potential means for risk communication. We define the task of visualization in risk communication, discuss the structure of macroprudential data, and review visualization techniques applied to systemic risk. We conclude that two essential, yet rare, features for supporting the analysis of big data and communication of risks are analytical visualizations and interactive interfaces. For visualizing the so-called macroprudential data cube, we provide the VisRisk platform with three modules: plots, maps and networks. While VisRisk is herein illustrated with five web-based interactive visualizations of systemic risk indicators and mod...

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“…For instance, evolutionary spectral clustering based algorithms [9,10,11], incremental K-means [12], selforganizing time map [13] and incremental kernel spectral clustering [14]. However, in all above-mentioned algorithms the 1 Corresponding author.…”

Section: Introductionmentioning

confidence: 99%