Forecasting Inflation In Indonesia Using The Modified Fuzzy Time Series Cheng

Inflation is one of the most important indicators to analyze a country’s economy. Therefore, it is necessary to forecast the inflation rate. Forecasting can be done by various methods, one of which is Fuzzy Time Series Cheng. In this study, several modifications were made to the method used. The purpose of this study is to forecast using the Modified Fuzzy Time Series (FTS) Cheng method and determine the accuracy of the forecasting results obtained. The results of this study indicate that the Modified FTS Cheng method can be used in forecasting, either by determining the interval average-based or using the Sturges equation. Based on the results of the calculation of forecasting accuracy using Mean Absolute Percentage Error (MAPE), the accuracy for Modified FTS Cheng by determining the average-based interval for forecasting based on the current state and next state is 11.58% and 5.78%, respectively. Furthermore, the Modified FTS Cheng by determining the interval using the Sturges equation resulted in a MAPE value of 9.61% and a FTS Cheng of 7.54%. The MAPE value of each method is less than 10%, which means that the method has a very good performance, except for Modified FTS Cheng by determining the average-based interval for forecasting based on current state has good performance with MAPE values ​​between 10 % and 20%.  Inflasi merupakan salah satu indikator penting yang digunakan dalam menganalisa perekonomian di suatu negara. Oleh karena itu, perlu dilakukan peramalan terhadap tingkat inflasi. Peramalan dapat dilakukan dengan berbagai metode, salah satunya Fuzzy Time Series Cheng. Pada penelitian ini dilakukan beberapa modifikasi pada metode yang digunakan. Tujuan penelitian ini adalah melakukan peramalan menggunakan metode Fuzzy Time Series Cheng yang Dimodifikasi dan menentukan akurasi dari hasil peramalan yang diperoleh. Hasil dari penelitian ini menunjukkan bahwa metode Fuzzy Time Series Cheng Dimodifikasi dapat digunakan dalam melakukan peramalan, baik dengan penentuan interval berbasis rata-rata maupun menggunakan persamaan Sturges. Berdasarkan hasil perhitungan keakuratan peramalan menggunakan Mean Absolute Percentage Error (MAPE) diperoleh akurasi untuk Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state dan next state masing-masing sebesar 11,58% dan 5,78%. Selanjutnya, Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval meggunakan persamaan Sturges menghasilkan nilai MAPE sebesar 9,61% dan Fuzzy Time Series Cheng sebesar 7,54%. Nilai MAPE dari masing-masing metode kurang dari 10% yang berarti bahwa metode tersebut mempunyai kinerja yang sangat baik, kecuali Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state mempunyai kinerja yang baik dengan nilai MAPE berada antara 10% dan 20%

Evolution strategies based coefficient of TSK fuzzy forecasting engine

Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance