Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting man...

Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand has direct impact on the performance of company. The goal study present new two-stage combination model named Hybrid-2-Best, for accurate forecasting. combines three models in single combined forecast. Hybrid-2-Best uses algorithm achieve better-performing forecasts. Case showed that proposed p...

Central Banks regularly make forecasts, such as the Fed’s Greenbook forecast, that are conditioned on hypothetical paths for the policy interest rate. While there are good public policy reasons to evaluate the quality of such forecasts, up until now, the most common approach has been to ignore their conditional nature and apply standard forecast e¢ ciency tests. In this paper we derive tests fo...

It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge thes...

The Masinga Reservoir located in the upper Tana River basin, Kenya, is extremely important in supplying the country’s hydropower and protecting downstream ecology. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. The Masinga dam’s operation is crucial in meeting power demands and thus contributing significantly to t...

Based on Monte Carlo simulations using both stationary and nonstationary data, a model selection approach which uses the SIC to select a "best" group of forecasts in the context of forecast combination regressions dominates a number of other techniques, including the standard t-statistic approach and the simple averaging rule. Our results are robust across various restricted and unrestricted ve...

This study investigates whether and why corporate managers have incentives to meet or slightly beat their own forecasts, and whether these incentives are related to their meeting or slightly beating analysts’ forecasts. After documenting that managers have incentives to meet or slightly beat their own forecasts, the paper formally models meeting or slightly beating forecasts as a signal that ma...

Budget forecasts are used by incumbent governments as a political tool to manage the elector particularly during an election cycle (Bruck and Stephan 2006). Incumbents apply budget forecasts strategically. They use short-term forecasts, which are more likely to be unbiased by political factors, annually. They rely on long-term forecasts, which are likely to be biased by political calculations, ...

Before 1960, little empirical research was done on forecasting methods. Since then, the literature has grown rapidly, especially in the area of judgmental forecasting. This research supports and adds to the forecasting guidelines proposed before 1960, such as the value of combining forecasts. New findings have led to significant gains in our ability to forecast and to help people to use forecas...