Spatio-temporal analysis of the extent of an extreme heat event

Evidence of global warming induced from the increasing concentration of greenhouse gases in the atmosphere suggests more frequent warm days and heat waves. The concept of an extreme heat event (EHE), defined locally based on exceedance of a suitable local threshold, enables us to capture the notion of a period of persistent extremely high temperatures. Modeling for extreme heat events is customarily implemented using time series of temperatures collected at a set of locations. Since spatial dependence is anticipated in the occurrence of EHE’s, a joint model for the time series, incorporating spatial dependence is needed. Recent work by Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) develops a space-time model based on a point-referenced collection of temperature time series that enables the prediction of both the incidence and characteristics of EHE’s occurring at any location in a study region. The contribution here is to introduce a formal definition of the notion of the spatial extent of an extreme heat event and then to employ output from the Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) modeling work to illustrate the notion. For a specified region and a given day, the definition takes the form of a block average of indicator functions over the region. Our risk assessment examines extents for the Comunidad Autónoma de Aragón in northeastern Spain. We calculate daily, seasonal and decadal averages of the extents for two subregions in this comunidad. We generalize our definition to capture extents of persistence of extreme heat and make comparisons across decades to reveal evidence of increasing extent over time

Forecasting Spikes in Electricity Prices

In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accurate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focuses exclusively on the prediction of spikes in electricity prices. The time series of price spikes is treated as a realization of a discrete-time point process and a nonlinear variant of the autoregressive conditional hazard (ACH) model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the sample period 1 March 2001 to 30 June 2007. The estimated model is then used to provide one-step-ahead forecasts of the probability of an extreme event for every half hour for the forecast period, 1 July 2007 to 30 September 2007, chosen to correspond to the duration of a typical forward contract. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.Electricity Prices, Price Spikes, Autoregressive Conditional Duration, Autoregressive

Predictive modeling of riverine constituent concentrations and loads using historic and imposed hydrologic conditions

This research was principally concerned with the task of quantifying dissolved and suspended constituents carried in river water when direct measurements are not available. This is a question of scientific and societal relevance, and one with a long history of study and a great deal of remaining difficulty. The traditional approach to estimating these quantities, linear regression models (LMs), suffers from poor flexibility and high subsequent bias in many applications. This research applied semiparametric generalized additive models (GAMs), a more flexible class of regression models, evaluated their performance in various locations and conditions, and applied them in a proactive modeling effort in a major water-supply reservoir. Chapter 1 compared GAMs to LMs for estimating nutrient and organic carbon loads in three major tributaries of the Wachusett Reservoir in central Massachusetts. The relative performance of each model was determined using cross-validation. GAMs outperformed LMs in most cases, explaining an additional 2% of load variance and 5% of concentration variance in validation data on average. Relative differences between the two modeling approaches exceeded 100% depending on the time interval of the load estimate. Chapter 2 assessed the applicability of GAMs to the prediction of riverine solute concentrations during extreme high-flow events when such events are absent from the models\u27 calibration data. The models tended to overpredict extreme-event concentrations, with increasing bias and variance for increasingly extreme hydrologic conditions. Despite an overall increase in uncertainty for extreme-event concentration estimates, estimates under extreme hydrologic conditions could be improved by taking into account the observed bias in the aggregated regional database. Chapter 3 developed and applied a methodology to generate reservoir tributary discharge and constituent concentration time-series for an imposed extreme-event scenario. A multivariate probability model was developed for constituent concentration in an arbitrary number of tributaries and water-quality constituents, conditional on time and hydrologic condition. Two separate historical storm events were modified using 3 extreme precipitation depths on tributaries of the Wachusett Reservoir Watershed in Massachusetts, U.S. Quasi-Monte Carlo was used to propagate this uncertainty to a process-based model of the receiving water body

Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa