Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

Abstract

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%.

Implementation of Fuzzy Black-Scholes Real Options Valuation Method on Nickel Smelter Investment Plan

“…Terdapat banyak penelitian yang menerapkan metode fuzzy, di antaranya [7] telah menerapkan model Fuzzy Times Series Markov Chain untuk meramalkan tingkat inflasi di Bandung, sementara [8] telah menerapkan metode Fuzzy Multi-Objective Linear Programming dalam masalah optimalisasi produksi kain batik, dan [9] telah menerapkan metode fuzzy mamdani untuk menentukan stok produk herbal berdasarkan permintaan dan penjualan . Adapun implementasi metode bilangan fuzzy pada real options, khususnya pada formula Black-Scholes, pertama kali diterapkan oleh [10].…”