Statistical analysis methods are generally derived under the assumption that forecast errors are strictly random and zero in the mean. If the short-term forecast, used as the background eld in the statistical analysis equation, is in fact biased, so will the resulting analysis be biased. The only way to properly account for bias in a statistical analysis is to do so explicitly, by estimating th...

Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main objective of this research is short term forecasting of energy price and consumption in Iranian ...

Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. A forecasting usually depends on many factors such as the product feature, supply chain constrain, market demand, market share, promotion strategy, competiti...

We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross-lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of fore...