The aim of this study was to develop short-term forecasts of the industrial production index in Brazil. Forecasts are made using five different methodologies: SARIMA, regressions, a structural, a dynamic factor models and ...

Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting ...

Abstract: We compare the medium-term GDP growth forecasts generated by experts to those generated by simple models. This study analyzes a large set of forecasts that covers 48 countries from 1997 to 2016. Out-of-sample ...

In a highly competitive market as the actual one, the production management must ensure that goods and services are supplied to the customers in right time, so they can offer a competitive advantage or, at least, can be ...

In this paprer, a multivariate polynomial (MP) combined with denoising techniques is proposed to forecast 1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is denoised by using ...

The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power ...

This article proposes that the combination of smoothing approach considering the entropic information provided by Renyi's method, has an acceptable performance in term of forecasting errors. The methodology of the proposed ...

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop ...

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function ...