Volatility is a key parameter used in many financial applications, from derivatives valuation to asset management and risk management. Volatility measures the size of the errors made in modeling returns and other financial variables. It was discovered that, for vast classes of models, the average size of volatility is not constant but changes with time and is predictable. Autoregressive conditi...

Most operations models assume that individuals have perfect beliefs about random variables or stochastic processes. In reality, however, individuals make judgment errors and are subject to predictable biases. We leverage two fundamental psychological principles bounded cognition and representativeness to model individuals' forecasting errors and biases in a way that is portable to operations mo...

In this research a model of forecasting the travel times of links will be addressed. Forecasting is one of the main topics of an integrated traffic management system and a necessity of dynamic route planning systems. To be able to forecast properly we use both historic data and current data from monitoring devices as input for our dynamic model. Thus, combining the best of both worlds, we are a...

Introduction In order to transition the forecasting, estimation and management of epidemic risks to individual administrative areas, the Agency for Consumer Rights Protection of Kazakhstan has developed a concept for modernizing the existing national system of the epidemiological surveillance (SES). It is proposed that the data from the SES (epidemiology, sanitary and epidemiological background...

Much has been written about the lack of transparency of computational intelligence models. This paper investigates the level of transparency of the Takagi-Sugeno neuro-fuzzy model and the Neural Network model by applying them to conflict management, an application which is concerned with causal interpretations of results. The neural network model is trained using the Bayesian framework. It is f...

This paper examines the association of comprehensive income with subsequent period net income as well as analysts’ earnings forecasts. Our results support the notion that comprehensive income is incrementally useful in predicting subsequent period changes in net income. We also document that comprehensive income is associated with analysts’ earnings forecast revisions and forecast errors. The e...

OBJECTIVE To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. METHODS Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy wa...

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after th...

Calving difficulty (dystocia) contributes heavily to losses in production in beef cow/calf herds. The obvious losses are due to calves or cows that die at or soon after calving. Less noticeable losses are due to delayed rebreeding, more open females, an extended calving season and increased labor costs. While occasional dystocia is almost unavoidable, through proper management, cattlemen can mi...

We investigate the association between errors in management forecasts of subsequent year earnings and current year accruals. In an uncertain operating environment, managers’ assessments of their firms’ business prospects are imperfect. Since managers’ imperfect business assessments influence both accruals generation and earnings projection, we hypothesize that management earnings forecasts exhi...