In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surf...

A number of previous studies have shown that a combination of forecasts typically outperforms any component forecast. Service managers may wish to use forecast combination to improve forecast accuracy in predicting retail sales. In this study, revenue data from an actual service company is used to generate and test a least absolute value (LAV) regression model for forecast combination. The LAV ...

In the framework of TEI@I methodology, this paper proposes a combined forecast method integrating contextual knowledge (CFMIK). With the help of contextual knowledge, this method considers the effects of those factors that cannot be explicitly included in the forecast model, and thus it can efficiently decrease the forecast error resulted from the irregular events. Through a container throughpu...

In this paper we want to analyse fuzzy weather forecasts, which are computed in our system and used to forecast pollution concentrations. The system works on real data: weather forecasts, meteorological situations and pollution concentrations. We compare defuzzification of the fuzzy weather forecast with weather forecast from Institute of Meteorology and Water Management. This comprehensive ana...

The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts. While the accuracy of the forecast error covariance matrix is crucial for achieving accurate forecasts, the estimate given by the EnKF needs to be improved using inflation techn...

This paper presents a forecasting model of unemployment based on labor force flows data that, in real time, dramatically outperforms the Survey of Professional Forecasters, historical forecasts from the Federal Reserve Board’s Greenbook, and basic time-series models. Our model’s forecast has a root-mean-squared error about 30 percent below that of the next-best forecast in the near term and per...

Each winter and early spring, the Northeast River Forecast Center (NERFC) faces the challenge of forecasting river stage and flow for ice affected rivers. “Stage versus flow” relationships tend to be in error and there is a lack of real-time information about the nature of the ice cover. The NERFC is investigating methods to forecast river stage and flow for ice-affected rivers because in times...