Real-world dynamic systems such as physical and atmosphereocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a “first-class citizen” of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new...

A computer model is built to simulate master production scheduling activities in a capacitated multi-item production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and th...

Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors Journal Article How to cite: Anacleto Junior, Osvaldo; Queen, Catriona and Albers, Casper (2013). Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(2) pp....

Future air traffic management (ATM) decision support systems (DSS) rely upon trajectory forecasting to adequately deliver benefits. These forecasts are subject to errors from a variety of sources. As the number and sophistication of ATM DSS capabilities grow, the interoperability between DSS will become more sensitive to the magnitude and variation in trajectory forecasting errors across DSS. T...

We propose a tracker-independent framework to determine time instants when a video tracker fails. The framework is divided into two steps. First, we determine tracking quality by comparing the distributions of the tracker state and a region around the state. We generate the distributions using Distribution Fields and compute a tracking quality score by comparing the distributions using the L1 d...

Typically, model misspecification is addressed by statistics relying on model-residuals, i.e., on one-step ahead forecasting errors. In practice, however, users are often interested in problems involving (also) multi-step ahead forecasting performances, which are not explicitly addressed by traditional diagnostics. In this article, we consider the topic of misspecification from the perspective ...

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast i...