Enabling External Factors for Inflation Rate Forecasting Using Fuzzy Neural System

Abstract

Inflation is the tendency of increasing prices of goods in general and happens continuously. Indonesia's economy will decline if inflation is not controlled properly. To control the inflation rate required an inflation rate forecasting in Indonesia. The forecasting result will be used as information to the government in order to keep the inflation rate stable. This study proposes Fuzzy Neural System (FNS) to forecast the inflation rate. This study uses historical data and external factors as the parameters. The external factor using in this study is very important, which inflation rate is not only affected by the historical data. External factor used are four external factors which each factor has two fuzzy set. While historical data is divided into three input variables with three fuzzy sets. The combination of three input variables and four external factors will generate too many rules. Generate of rules with too many amounts will less effective and have lower accuracy. The novelty is needed to minimalize the amount of rules by using two steps fuzzy. To evaluate the forecasting results, Root Means Square Error (RMSE) technique is used. Fuzzy Inference System Sugeno used as the comparison method. The study results show that FNS has a better performance than the comparison method with RMSE that is 1.81.

Extreme Learning Machine and Particle Swarm Optimization for Inflation Forecasting

“…Furthermore, the forecasting process will be carried out by the ELM method. The fitness formula that will be used in the equation 11is as follows: (11) The initial process of the system is the initialization of particles at PSO, particles randomly generated between 0-1 in the form of real code numbers. These particles are used as weights from the input layer to the hidden layer.…”

“…These particles are used as weights from the input layer to the hidden layer. Then calculate the value of fitness based on equation (11). After knowing the fitness value then determined the value of PBest and Gbest, after that updates the velocity, then updates the position and calculates fitness again.…”

Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model

“…This study will also use some external factors to determine the level of inflation. Some external factors used in this study include the Consumer Price Index (CPI), Money Supply, the BI rate, and Exchange Rate [7].…”

System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model

“…One of the major problems in NN modeling in time series data is the need for selecting a proper initial data processing. The combination of wavelets, as an initial processing method and NN as a method that processes inputs into an output, produces a hybrid model known as Wavelet Neural networks (WNN) [19]- [25]. The application of the WNN model for time series forecasting is one of the most interesting research topics in the fields of mathematics, statistics, and computer science.…”

“…In another hand, some researchers who have implemented the hybrid method between wavelet and NN, or hybrid among machine learning methods for time series forecasting ie Bunnoon [22] has forecasted the electricity peak load demand, Poorani and Murugan [23] have forecasted the rising demand for electric vehicles applicable to Indian road conditions, Kamley, et al [24] have measured the performance forecasting of the share market, and the enabling external factors for inflation rate forecasting were conducted by Sari, et al [25]. In the previous hybrid methods that were not a hybrid between wavelet and RBFNN.…”