Applied Mathematics, 2019, 10, 704-717 http://www.scirp.org/journal/am ISSN Online: 2152-7393 ISSN Print: 2152-7385 DOI: 10.4236/am.2019.108050 Aug. 27, 2019 704 Applied Mathematics Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes Julián Pucheta 1 , Carlos Salas 2 , Martín Herrera 2 , Cristian Rodriguez Rivero 3 , Gustavo Alasino 4 1 FCEFyN-Universidad Nacional de Córdoba, Córdoba, Argentina 2 FTyCA-Universidad Nacional de Catamarca, Catamarca, Argentina 3 Cristian University of California, Los Angeles, USA 4 Universidad Torcuato Di Tella, Buenos Aires, Argentina 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 un- derlying 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. Keywords Stochastic Analysis, Time Series Forecasting, Decision Making, Dynamic Process, Process Modelling 1. Introduction The agribusiness activities are the engine where the vegetal production lies with its decision-making built-in [1]. In Argentina, the activity’s profit is subjected to a good production plan which in turn is subjected to financial variables [2]. One of them is the Emerging Market Bond Index (EMBI) known as country risk. This variable indexes the economic health of the country and is a strong signal when is compared against that of Chile, Brazil and Mexico countries whose products compete with those of Argentinian. When the producer must perform the production plan, there arises the need for counting with information about the EMBI values with some future horizon. In this paper, a method to forecast time series from EMBI with short and long term horizons is proposed. How to cite this paper: Pucheta, J., Salas, C., Herrera, M., Rodriguez Rivero, C. and Alasino, G. (2019) Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes. Applied Ma- thematics, 10, 704-717. https://doi.org/10.4236/am.2019.108050 Received: July 25, 2019 Accepted: August 24, 2019 Published: August 27, 2019 Copyright © 2019 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access