Article citationsMore>>

Rivero, C.R., Pucheta, J., Laboret, S. and Sauchelli, V. (2017) Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets. Journal of Artificial Intelligence and Soft Computing Research, 7, 5-16.
https://doi.org/10.1515/jaiscr-2017-0001

has been cited by the following article:

• TITLE: Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

  AUTHORS: Julián Pucheta, Carlos Salas, Martín Herrera, Cristian Rodriguez Rivero, Gustavo Alasino

  KEYWORDS: Stochastic Analysis, Time Series Forecasting, Decision Making, Dynamic Process, Process Modelling

  JOURNAL NAME: Applied Mathematics, Vol.10 No.8, August 27, 2019

  ABSTRACT: This paper intends to develop suitable methods to provide likely scenarios in order to support decision making for slow dynamic processes such as the underlying of agribusiness. A new method to analyze the short- and long-term time series forecast and to model the behavior of the underlying process using nonlinear artificial neural networks (ANN) is presented. The algorithm can effectively forecast the time-series data by stochastic analysis (Monte Carlo) of its future behavior using fractional Gaussian noise (fGn). The algorithm was used to forecast country risk time series for several countries, both for short term that is 30 days ahead and long term 350 days ahead scenarios.