I n 2003, the Meteorological Service of Canada (MSC) began a significant restructuring of its forecasting operations in response to financial pressures. Senior management proposed that the MSC could be made more cost effective while continuing to provide quality services by pursuing a more centralized forecasting approach and increasing the automation of forecasts via numerical weather predicti...

L abor costs have recently come under scrutiny by policymakers, business economists, and financial market participants. The primary concern has been that tight labor markets might lead to faster compensation growth and, ultimately, to upward pressure on general inflation. The employment cost index (ECI) has received particularly close attention because many analysts consider it to be one of the...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships fiom the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...

Forecasting electricity consumption is one of the most important operational issues in order to the use facility systems and power sources optimally. Electricity demand forecasting process will ultimately have an important role in the economic and security of the energy operating system. The objectives of this research are to forecast long-term electricity demand for 2011-2022 and to provide ma...

The objectives of this paper are two folds. The first one is to improve the time forecasting produced from the well known Earned Value Management (EVM), using the polynomial function. The time prediction observed from the polynomial model, which is compared against that observed from the most common method for time forecasting (critical path method), is a more accurate (mean absolute percentage...

Resum The volume and availability of business and finance data may continue to increase in the near future. However, the utility of such data is by no means straightforward due to a lack of integration between data-driven techniques and usual decision-making processes. This paper aims to integrate data with multiobjective decision-making in cash management by means of machine learning. To this ...

Resum The volume and availability of business and finance data may continue to increase in the near future. However, the utility of such data is by no means straightforward due to a lack of integration between data-driven techniques and usual decision-making processes. This paper aims to integrate data with multiobjective decision-making in cash management by means of machine learning. To this ...

Resum The volume and availability of business and finance data may continue to increase in the near future. However, the utility of such data is by no means straightforward due to a lack of integration between data-driven techniques and usual decision-making processes. This paper aims to integrate data with multiobjective decision-making in cash management by means of machine learning. To this ...