scholarly journals Pendekatan Fuzzy Pada Peramalan Jumlah Kunjungan Wisatawan Mancanegara ke Kabupaten Badung

2016 ◽  
Vol 6 (2) ◽  
pp. 126
Author(s):  
Tjokorda Bagus Oka ◽  
Eka N. Kencana
Keyword(s):  
Transition Matrix ◽  
Historical Data ◽  
Fuzzy Model ◽  
Logical Relationship ◽  
Forecasting Accuracy ◽  
Member Function ◽  
Markov Transition Matrix ◽  
Forecasting Error ◽  
Markov Transition ◽  
Triangular Number

This work is directed to forecast the number of foreign visitors come to tourist’s destinations at Badung regency, Province of Bali. Using visit historical data for period January 2000 to February 2015, Markov Transition Matrix and Fuzzy Triangular Number are applied to represent fuzzy logical relationship group and member function in fuzzy model, respectively.  The results showed the in-sample forecasting accuracy for our fuzzy model as much as 2,48 percent. To validate the model, we found the average forecasting error rate for five consecutive months (March – July 2015) as much as 2,70 percent.

scholarly journals Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 281
Author(s):  
Nazirah Ramli ◽  
Siti Musleha Ab Mutalib ◽  
Daud Mohamad
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Fuzzy Numbers ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Proposed Model ◽  
Fuzzy Forecast

This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

scholarly journals K-theory for Cuntz-Krieger algebras arising from real quadratic maps

2003 ◽  
Vol 2003 (34) ◽  
pp. 2139-2146 ◽  
Author(s):  
Nuno Martins ◽  
Ricardo Severino ◽  
J. Sousa Ramos
Keyword(s):  
Transition Matrix ◽  
Parameter Family ◽  
Quadratic Maps ◽  
Markov Transition Matrix ◽  
Kneading Sequence ◽  
Markov Transition ◽  
K Theory ◽  
The One ◽  
Real Quadratic

We compute theK-groups for the Cuntz-Krieger algebras𝒪A𝒦(fμ), whereA𝒦(fμ)is the Markov transition matrix arising from the kneading sequence𝒦(fμ)of the one-parameter family of real quadratic mapsfμ.

Constrained adaptive Markov transition matrix based target tracking with IMMPF

2013 ◽  
Vol 100 (11) ◽  
pp. 1569-1578
Author(s):  
Fei Wang ◽  
Mathini Sellathurai ◽  
David Wilcox ◽  
Jianjiang Zhou
Keyword(s):  
Target Tracking ◽  
Transition Matrix ◽  
Markov Transition Matrix ◽  
Markov Transition

scholarly journals Empirical Convergence Rate of a Markov Transition Matrix

2019 ◽  
pp. 1-7
Author(s):  
Steven T. Garren
Keyword(s):  
Convergence Rate ◽  
Confidence Intervals ◽  
Transition Matrix ◽  
Current Time ◽  
Computing Power ◽  
Largest Eigenvalue ◽  
Markov Transition Matrix ◽  
Time Period ◽  
Markov Transition ◽  
Second Largest Eigenvalue

The convergence rate of a Markov transition matrix is governed by the second largest eigenvalue, where the first largest eigenvalue is unity, under general regularity conditions. Garren and Smith (2000) constructed confidence intervals on this second largest eigenvalue, based on asymptotic normality theory, and performed simulations, which were somewhat limited in scope due to the reduced computing power of that time period. Herein we focus on simulating coverage intervals, using the advanced computing power of our current time period. Thus, we compare our simulated coverage intervals to the theoretical confidence intervals from Garren and Smith (2000).

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