Inflation rate modeling: Adaptive neuro-fuzzy inference system approach and particle swarm optimization algorithm...

Area of Science:

• Economics
• Computational Intelligence

Background:

• Inflation is a critical variable in economic activity and analysis.
• Accurate inflation rate modeling is essential for developing effective anti-inflationary policies.
• Existing models may benefit from enhanced predictive capabilities.

Purpose of the Study:

• To develop a robust model for predicting Iran's production inflation rate.
• To leverage time series data for improved inflation forecasting.
• To enhance the quality of inflation modeling through hybrid computational intelligence techniques.

Main Methods:

• Adaptive Neuro-Fuzzy Inference System (ANFIS) for modeling.
• Particle Swarm Optimization (PSO) algorithm for ANFIS training.
• Utilizing time series data from the Central Bank of the Islamic Republic of Iran (1986-2018).

Main Results:

• A trained ANFIS-PSO model was successfully developed for Iran's inflation rate.
• The combination of ANFIS and PSO improved the quality of inflation modeling.
• The model provides a tool for analyzing inflation formation.

Conclusions:

• The ANFIS-PSO hybrid approach offers improved inflation rate modeling.
• This model can be utilized by researchers in macroeconomics, monetary economics, and public sector economics.
• The developed model supports the creation of targeted anti-inflation programs.