Previento is an operational forecast system which provides a prediction of the expected power output for a time horizon up to 48 hours. It is based on an physical approach with input from a large scale weather prediction model like Lokalmodell of the German Weather Service. In this paper we focus on the forecast of power output of regional distributed wind farms. Due to spatial smoothing effect...

This paper brings together several strands of the inflation literature. Specifically, this paper uses the forecast from a random walk model of inflation as a benchmark to test and compare the forecast performance of several alternatives including the Greenbook forecast by the Fed staff, the Survey of Professional Forecasters median forecast, lagged CPI inflation minus food and energy, lagged CP...

Four-dimensional variational (4D-Var) data assimilation method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by usin...

This paper applies receiver operating characteristics (ROC) analysis to M3 Competition, micro monthly time series for one-month-ahead forecasts. Using the partial area under the curve (PAUC) criterion as a forecast accuracy measure and paired-comparison testing via bootstrapping, we find that complex methods (AutomatANN, Flores-Pearce2, Forecast ProSmart FCS, and Theta) perform best for forecas...

Using a standard factor decomposition of a panel of forecasts, we have shown that the forecast uncertainty from the standpoint of a policy maker can be expressed as the disagreement among forecasters plus the perceived variability of common aggregate shocks. Thus, the uncertainty of the average forecast is not the variance of the average forecast but rather the average of the variances of the i...

Four dimensional variational (4D-Var) Data Assimilation (DA) method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by...

In this paper, we study a forecast method for demand quantities in the high-technology industry. We formulate and test a parameter-driven scheme to generate forecasts. We forecast demand quantities for several future periods using the formula F = aX + (1a)Y where a is a real number from [0,1] to be estimated, X is a simple exponential smoothing forecast based on historical data, and Y are the f...

Sugarcane is one of the severely perishable crops that is used as raw material for white sugar production. Sucrose content of the sugarcane which is of high commercial value decreases in quality due to pre-harvest burning, high ambient temperature, kill-to-mill delays as well as microbial contaminations. Delays in sugarcane transportation are the most important risks which can affect the qualit...

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system, is based on a subset of analogue years drawn from the full climatological distribution. In this stud...