Empirical tests of forecast optimality have traditionally been conducted under the assumption of mean squared error loss or some other known loss function. This paper establishes new testable properties that hold when the forecaster’s loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against co...

Our incentive is to study the behaviour of lot-sizing rules in a multilevel context when forecast demand is subject to changes within the forecast window. To our knowledge, only Bookbinder and Heath (1988) proposed a lot-sizing study in a multi-echelon rolling schedule with probabilistic demands. But their simulation study was limited to two arborescent structures with 6 nodes. By means of an e...

Abstract: With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation characteristics of the PV power ...

The main goal of this paper is to study statistical indices and evaluate AGC indices in power system which has large penetration of the WTGs. Increasing penetration of wind turbine generations, needs to study more about impacts of it on power system frequency control. Frequency control is changed with unbalancing real-time system generation and load . Also wind turbine generations have more flu...

Forecast rationality under squared error loss implies various bounds on second moments of the data across forecast horizons. For example, the mean squared forecast error should be increasing in the horizon, and the mean squared forecast should be decreasing in the horizon. We propose rationality tests based on these restrictions, including new ones that can be conducted without data on the targ...

Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method ...

Efficient market hypothesis predicts that capital markets are beset with cer-tain biases which result from wrong estimation, and negatively influence shareholders’ expectations for higher returns, which in turn affects invest-ment efficiency, financial constraints and corporate performance efficacy in competitive markets, and eventually mitigates firm value. The present study aims at examining ...

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 the fore...

In a non-stationary world subject to structural breaks, where model and mechanism differ, equilibrium-correction models are a risky device from which to forecast. Equilibrium shifts entail systematic forecast failure, and indeed forecasts will tend to move in the opposite direction to the data. A new explanation for the empirical success of second differencing is proposed. We consider model tra...

Using a scenario of multiple mobile observing platforms (UAVs) measuring weather variables in distributed regions of the Pacific, we are developing algorithms that will lead to improved forecasting of high-impact weather events. We combine technologies from the nonlinear weather prediction and planning/control communities to create a close link between model predictions and observed measurement...