Author(s)

Julián Pucheta¹, Carlos Salas², Martín Herrera², Cristian Rodriguez Rivero³, Gustavo Alasino⁴

¹FCEFyN-Universidad Nacional de Córdoba, Córdoba, Argentina.
²FTyCA-Universidad Nacional de Catamarca, Catamarca, Argentina.
³Cristian University of California, Los Angeles, USA.
⁴Universidad Torcuato Di Tella, Buenos Aires, Argentina.

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.

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

Pucheta, J. , Salas, C. , Herrera, M. , Rivero, C. and Alasino, G. (2019) Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes. Applied Mathematics, 10, 704-717. doi: 10.4236/am.2019.108050.