This paper studies charging scheduling problem of electric vehicles (EVs) in the scale of a microgrid (e.g., a university or town) where a set of charging stations are controlled by a central aggregator. A bi-objective optimization problem is formulated to jointly optimize total charging cost and user convenience. Then, a close-to-optimal online scheduling algorithm is proposed as solution. The...

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk based on theories of decision-making under uncertainty, which won the 2002 Nobel Prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of o...

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (AP ST), which is a two step approach to predict the direction of change of stock price indices. First, performs da...

Artificial neural networks in time series prediction generally minimize a symmetric statistical error, such as the sum of squared errors, to model least squares predictors. However, applications in business elucidate that real forecasting problems contain non-symmetric errors. In inventory management the costs arising from overversus underprediction are dissimilar for errors of identical magnit...

We revisit the concept of unpredictability to explore its implications for forecasting strategies in a non-stationary world subject to structural breaks, where model and mechanism differ. Six aspects of the role of unpredictability are distinguished, compounding the four additional mistakes most likely in estimated forecasting models. Structural breaks, rather than limited information, are the ...

In a comment to our article entitled ‘‘Forecasting Pharmaceutical Prices for Economic Evaluations When There Is No Market: A Review’’, which was recently published in PharmacoEconomics Open [1], Holko stated that marketspecific aspects could affect efficiency of forecasting price using the price of a close substitute [2]. We thank the author for the comment on our article and agree that there a...

This paper presents a novel approach to financial time series analysis and prediction. It is mainly devoted to the problem of forecasting university facility and administrative cost recovery. However, it can also be used in other areas of a similar nature. The methodology incorporates a two-stage hybrid mechanism for selection of prediction-relevant features and for forecasting based on this se...

[//121] The Delphi technique of aggregating the forecasts of a number of experts on multidisciplinary issues/1/ is a recent development in long-range forecasting. By sequentially polling the experts' opinions, interspersed with feedback of information on the just-previous poll to the experts, consensus is generated. Meanwhile, anonymity is guaranteed in the polling procedures so that social psy...

Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but...

It has been claimed that the foundational questions about the Delphi technique of longrange forecasting have all been answered. However, two unanswered foundational questions are considered in this paper. As a problem in the selection of experts, empirical evidence is adduced showing that institutionally sanctioned expertise (e.g. the "professional") in long-range value forecasting is not cost-...