We present a platform built on large-scale, data-centric machine learning (ML) approaches, whose particular focus is demand forecasting in retail. At its core, this platform enables the training and application of probabilistic demand forecasting models, and provides convenient abstractions and support functionality for forecasting problems. The platform comprises of a complex end-to-end machin...

In this paper the problem of forecasting high dimensional time series is considered. Such time series can be modeled as matrices where each column denotes a measurement. In addition, when missing values are present, low rank matrix factorization approaches are suitable for predicting future values. This paper formally defines and analyzes the forecasting problem in the online setting, i.e. wher...

How useful are probabilistic forecasts of the outcomes of particular situations? Potentially, they contain more information than unequivocal forecasts and, as they allow a more realistic representation of the relative likelihood of different outcomes, they might be more accurate and therefore more useful to decision makers. To test this proposition, I first compared a SquaredError Skill Score (...

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecas...

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks ...

In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, omni-directional prediction is more scientific than single point or interval prediction. this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), Gaussian process regression (GPR) pro...

Effective time-series forecasting methods are of significant importance to solve a broad spectrum research problems. Deep probabilistic techniques have recently been proposed for modeling large collections time-series. However, these explicitly assume either complete independence (local model) or dependence (global between in the collection. This corresponds two extreme cases where every is dis...