scholarly journals KOMPARASI KINERJA FUZZY TIME SERIES DENGAN MODEL RANTAI MARKOV DALAM MERAMALKAN PRODUK DOMESTIK REGIONAL BRUTO BALI

2014 ◽  
Vol 3 (3) ◽  
pp. 116
Author(s):  
I MADE ARYA ANTARA ◽  
I PUTU EKA N. KENCANA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Error Rate ◽  
Original Data ◽  
Rate Of Change ◽  
Fuzzy Time Series ◽  
Domestic Product ◽  
Quarterly Data ◽  
Forecasting Methods ◽  
Soft Modeling

This paper aimed to elaborates and compares the performance of Fuzzy Time Series (FTS) model with Markov Chain (MC) model in forecasting the Gross Regional Domestic Product (GDRP) of Bali Province.  Both methods were considered as forecasting methods in soft modeling domain.  The data used was quarterly data of Bali’s GDRP for year 1992 through 2013 from Indonesian Bureau of Statistic at Denpasar Office.  Inspite of using the original data, rate of change from two consecutive quarters was used to model. From the in-sample forecasting conducted, we got the Average Forecas­ting Error Rate (AFER) for FTS dan MC models as much as 0,78 percent and 2,74 percent, respec­tively.  Based-on these findings, FTS outperformed MC in in-sample forecasting for GDRP of Bali’s data.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

scholarly journals Disaster Forecasting Approach In Indonesia: A Fuzzy Time Series - Markov Chain Model

2019 ◽  
Author(s):  
Rahmad Syah
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Natural Disasters ◽  
Markov Chain Model ◽  
Volcanic Eruptions ◽  
Fuzzy Time Series ◽  
Chain Model ◽  
The Past ◽  
The Future ◽  
Transport Accidents

The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.

scholarly journals Prediksi Harga Jagung Menggunakan Metode Fuzzy Time Series Dengan Atau Tanpa Menggunakan Markov Chain

2021 ◽  
Vol 4 (1) ◽  
pp. 49-54
Author(s):  
Danung Nur Adli
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data

Penelitian ini dilakukan dengan tujuan memprediksi harga pakan jagung menggunakan salah satu model matematika yang disebut fuzzy time series. Data yang didapatkan yaitu data historis atau rentan waktu dari berbagai literasi seperti hargaweb.id, jagungbisi.com, dan BPS dari tahun 2020-2021, kuarter pertama. Data tersebut nantinya akan dijadikan bahan perhitungan. Data dianalisa menggunakan R Studio. Kemudian algoritma fuzzy time series. hasil penelitian fuzzy time series menghasilkan prediksi harga pada jagung menggunakan time series. Memprediksi harga akan cenderung berubah dari kisaran Rp/ 4.000-4.400,- yang mana tingkat error hanya ada di level 8,23.Logika fuzzy atau time series mampu menyajikan prediksi harga jagung pada tahun 2020-2021 dengan keakuratan dengan tingkat error 8.23% artinya tidak berbeda jauh. Kedepannya banyak model matematika yang bisa digunakan untuk memprediksi dari harga bahan baku atau aspek lainnya pada industri peternakan.

scholarly journals Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia

2022 ◽  
Vol 335 ◽  
pp. 00016
Author(s):  
Osfar Sjofjan ◽  
Danung Nur Adli
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Square ◽  
Forecast Method ◽  
Short Term ◽  
Bird Nest ◽  
Markov Chain Method

Edible bird nest (EBN) were traditional medicine consumed by the Tiongkok. This study compared two-algorithm method. Fuzzy time series and Markov chain as forecast method the number of bird nest exported from Indonesia. The secondary data between 2012 and 2018 were from Bureau Central Statistic (BPS). The scope using in this study were bird nest between 2012 until 2018, with a unit of volume kilograms (Kg). Used secondary export data, collected from BPS of Indonesia. Data were analysed using Fuzzy Time Series with and without Markov Chain using R Studio. The result showed that Fuzzy Time Series with and without Markov Chain method performs better in the forecasting ability in short-term period prediction and the values of Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) tends to be smaller than the Fuzzy Time Series without Markov Chain. It can be concluded the number of exported can be used Fuzzy time series.

AVERAGE-BASED FUZZY TIME SERIES MARKOV CHAIN IN JCI FORECASTING

2016 ◽  
Vol 100 (5) ◽  
pp. 661-669
Author(s):  
Ro’fah Nur Rachmawati ◽  
Johari ◽  
Ashadi Salim
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Fuzzy Time Series
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