We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called "artificial...

The main objective of the paper is to develop an econometric model to forecast box-office revenue of motion pictures. Considering the importance demand for new products, marketing researches have developed various demand forecasting models. However, these models forecast future demands based on either several months of initial sales data after new product introduction or the survey data on cust...

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a ‘‘prior’’ forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or ‘‘regularized’’ categoric...

Forecasting of time series data is crucial for decision-making processes in many domains as it allows the prediction of future behavior. In this context, a model is fit to the observed data points of the time series by estimating the model parameters. The computed parameters are then utilized to forecast future points in time. Existing approaches integrate forecasting into traditional relationa...

A watershed simulation and forecasting system is widely used in Finland for simulation of hydrological cycle and for making real-time forecasts. The system is based on a watershed model. This was based originally on the HBV-model and simulates the hydrological cycle using standard meteorological data. The model simulates the whole land area of Finland, including cross-border watersheds. This is...

Fossil energy markets have always been known as strategic and important markets. They have a significant impact on the macro economy and financial markets of the world. The nature of these markets are accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted by using appropriate tools. This paper adopts the Generalized Auto Regressive Condition...

Applying 4-levels grid nested WRF (Weather Research and Forecasting) model to predict meteorological elements of wind farm field, and employing WRF model outputs and SCADA data of wind turbines to construct wind power forecast models by using the APLSR (Adapting Partial Least Square Regression) method, this paper has conducted wind power forecast in 24 hours ahead, at Wenling wind farm which lo...

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized us...

This study proposes the model for interpolating time series to use them  to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in  M3-Competition data. Comparing  to the exist...