ABSTRACT Inflation, defined as the trend of the continuous increasing of the general level of prices within a country's economy during a time period, affects both the private and public sectors of the economy. Policy makers have the need to control and stabilize the rate of inflation at low levels to achieve economic growth and prosperity. Particularly, they use the rate of inflation as a measure to diagnose economic problems and then to apply the corresponding macroeconomic policies. So, inflation rate forecasting must be accurate, and the measurement of the inflation rate, which is usually dependent on the consumer price index (CPI), should be as accurate as possible. Although there are many different ways to anticipate the CPI, the most accurate methods are those that use artificial neural network models. These methods usually outperform the traditional statistical‐based forecasting techniques. This study spans the period from January 2015 to December 2024 using monthly, non‐seasonally adjusted data from the Organization for Economic Co‐operation and Development (OECD). Since WASD (weights and structure determination) neural networks have been demonstrated to address the drawbacks of traditional back‐propagation neural networks, like poor training speed and local minimum, a three‐layer power‐activation WASD neural network model, termed WASDCPI, is taken into consideration. The WASDCPI model performs better than other well‐known machine learning techniques for predicting the CPI of countries, including the USA, UK, Germany, France, India, Switzerland, Korea, and the Slovak Republic. All the data analysis is being conducted using the MATLAB environment.
Jerbi et al. (Mon,) studied this question.