Understanding Insured Behavior through Causal Impact Analysis of IoT Streams

Toman, Patrick; Soliman, Ahmed; Ravishanker, Nalini; Rajasekaran, Sanguthevar; Lally, Nathan; D’Addeo, Hunter

doi:10.1109/iscsic60498.2023.00078

2023 7th International Symposium on Computer Science and Intelligent Control (ISCSIC) 2023

DOI: 10.1109/iscsic60498.2023.00078

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Understanding Insured Behavior through Causal Impact Analysis of IoT Streams

Patrick Toman,

Ahmed Soliman,

Nalini Ravishanker

et al.

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Cited by 2 publications

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Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

Chen

Равишанкер

2023

Future Internet

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With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data. This paper proposes a feature-based clustering approach using topological data analysis, which is a set of methods for finding topological structure in data. Persistence diagrams and landscapes are popular topological summaries that can be used to cluster time series. This paper presents a framework for selecting an optimal number of persistence landscapes, and using them as features in an unsupervised learning algorithm. This approach reduces computational cost while maintaining accuracy. The clustering approach was demonstrated to be accurate on simulated data, based on only four, three, and three features, respectively, selected in Scenarios 1–3. On real data, consisting of multiple long temperature streams from various US locations, our optimal feature selection method achieved approximately a 13 times speed-up in computing.

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Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

Chen

Равишанкер

2023

Future Internet

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Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

Toman,

Ravishanker,

Lally

et al. 2023

Future Internet

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With the advent of the “Internet of Things” (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler’s (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program’s scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.

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Understanding Insured Behavior through Causal Impact Analysis of IoT Streams

Cited by 2 publications

References 14 publications

Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

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