Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational errors are typically not accounted for. This paper introduces a way to address these limitations by coupling...

Conventionally, linear and univariate time series models are broadly used in earning forecast. However, their forecasting accuracies are seriously limited without considering sufficient important factors. On the other hand, using more variables does not guarantee to obtain better forecasting accuracy and may cause inefficiency. The Multi-Objective Genetic Algorithms (MOGA) have been shown to be...

The logistic model has been very effective in forecasting many technological forecasting patterns. However, it has the characteristic of underestimating the forecasts in many situations. This is mainly due to the constraints imposed by the constant saturation level of the logistic growth curve. This paper proposes a variable asymptote logistic (VAL) model for forecasting electricity consumption...

Horizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. Analyzing forecasting errors and...

earned value management (evm) is the process of integrating the time and cost management within the framework of project scope management. the earned value has provided methods for predicting the cost for projects. in large part, these methods have not been improved upon since their beginnings and remain unsubstantiated as to accuracy. in this direction, several mathematics formulas have been d...

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)'s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essent...

hydropower reservoirs Georgia Papacharalampous*, Hristos Tyralis, and Demetris Koutsoyiannis Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece * Corresponding author, [email protected] Abstract: Multi-step ahead streamflow forecasting is of practical i...

Can people accurately predict how they will act in a moral dilemma? Our research suggests that in some situations, they cannot, and that emotions play a pivotal role in this dissociation between behavior and forecasting. In the current experiment, individuals in a moral action condition cheated significantly less on a math task than participants in a forecasting condition predicted they themsel...

This paper proposes a novel ensemble learning approach based on logistic regression (LR) and artificial intelligence tool, i.e. support vector machine (SVM) and back-propagation neural networks (BPNN), for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM and BPNN are introduced. Then, the forecasting results by LR are introduce...

This paper develops new fundamental models for forecasting presidential, senatorial, and gubernatorial elections at the state level using fundamental data. Despite the fact that our models can be used to make forecasts of elections earlier than existing models and they do not use data from polls on voting intentions, our models have lower out-of-sample forecasting errors than existing models. O...