Abstract

The paper presented different exchange rate forecasting models based on fundamental economics using different aggregation information operators such as heavy moving average, forgotten effects and expertons. The heavy ordered weighted moving average weighted average (HOWMAWA) operator is introduced. This new operator includes the weighted average in the typical heavy ordered weighted moving average (HOWMA) operator, considering a degree of importance for each concept that includes the operator. The use of expertons and forgotten effects methodology represents the information of experts in the field, with which hidden variables or second-degree relations were obtained. Once these items were detected, they were included in the econometric models, and the forecast of exchange rate of USD/MXN was performed using time series and HOWMA and HOWMAWA operators. The results show that the inclusion of the forgotten effects and heavy moving average operators improves our results and reduces the forecast error.