ALGORITHM TO DEVELOP SERIES AUTOMATIC FORECASTS UNIVARIATE TIME. THE NATIONAL INDEX OF MEXICO CONSUMER PRICES

Abstract

The present study compares the accuracy of automatic univariate time series forecasting methods for the value of the National
Consumer Price Index of Mexico, for which monthly data from 1969 to 2021 are used. Two of the main methods are presented in
the study area: 1) The Exponential Smoothing State Spatial Models (ETS) and 2) The Autoregressive Integrated and Moving
Average (ARIMA) models. A computational algorithm is developed for the identification of a data generating process to estimate
the optimal parameters. The second method presents a better performance in the calculation of the predicted values. The results
show the usefulness of automatic forecasting methods when proper procedures are followed.