Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

2014 ◽  
Vol 18 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Fuzzy Time Series ◽  
Economic Systems ◽  
Random Data ◽  
Human Behaviors ◽  
Section Data ◽  
Tick Data ◽  
Economic Analyses ◽  
Fuzzy Random

Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

Fuzzification Methods and Prediction Accuracy of Fuzzy Autocorrelation Model

2017 ◽  
Vol 21 (6) ◽  
pp. 1009-1016 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Model Prediction ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
High Accuracy ◽  
Fuzzy Time Series ◽  
Ar Model ◽  
Series Data ◽  
High Prediction

The fuzzy autocorrelation model is a fuzzified autoregressive (AR) model. The aim of the fuzzy autocorrelation model is to describe the possible states of the system with high accuracy. This model uses autocorrelation similar to the Box–Jenkins model. The fuzzy autocorrelation model occasionally increases the vagueness. Although the problem can be mitigated using fuzzy confidence intervals instead of fuzzy time-series data, the unnatural estimations do not improve. Subsequently, an alternate method was used to fuzzify the time-series data and mitigate the unnatural estimation problem. This method also improved the model prediction accuracy. This paper focuses on fuzzification method, and discusses the prediction accuracy of the model and fuzzification of the time-series data. The analysis of the Nikkei stock average shows a high prediction accuracy and manageability of a fuzzy autocorrelation model. In this pape, a quartile is employed as an alternate fuzzification method. The model prediction accuracy and estimation behavior are verified through an analysis. Finally, the proposed method was found to be successful in mitigating the problems.

scholarly journals Estimation Model And Selection Method Of Panel Data Regression : An Overview Of Common Effect, Fixed Effect, And Random Effect Model

2018 ◽  
Author(s):  
rizka zulfikar
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Unbiased Estimation ◽  
Cross Section Data ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression

The estimation in the regression analysis with cross section data is done by estimating the least squares method called Ordinary Least Square (OLS). Regression Method Data Panel will give the result of estimation which is Best Linear Unbiased Estimation (BLUE). Data Panel Regression is a combination of cross section data and time series, where the same unit cross section is measured at different times. So in other words, panel data is data from some of the same individuals observed in a certain period of time. If we have T time periods (t = 1,2, ..., T) and N the number of individuals (i = 1,2, ..., N), then with panel data we will have total observation units of N x T. If sum unit time is the same for each individual, then the data is called balanced panel. If instead, the number of time units is different for each individual, then it is called the unbalanced panel. While other data types, namely:time-series data and cross-section. In time series, one or more variables will be observed on one observation unit within a certain time frame. While data cross-section is the observation of several units of observation in a single point of time.Unlike the usual regression, panel data regression must go through the precise estimation modeling step.SEE ALSO :Zulfikar, R., & Mayvita, P. A. (2017). THE EFFECTS OF POLITICAL EVENTS AGAINST ABNORMAL RETURN AND TOTAL VOLUME SHARIA SHARES ACTIVITY THAT LISTED IN JAKARTA ISLAMIC INDEX (JII). JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen, 14(02), 64-74.Zulfikar, R., & AdeMayvita, P. (2017). Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII). Wiga: Jurnal Penelitian Ilmu Ekonomi, 7(2), 108-124.

Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation

2016 ◽  
Vol 20 (4) ◽  
pp. 512-520 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
◽  
Takayuki Kawaura ◽  
Junzo Watada ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Predictive Accuracy ◽  
Fuzzy Theory ◽  
Consumer Price Index ◽  
Time Series Model ◽  
Fuzzy Regression ◽  
Fuzzy Time Series ◽  
Time Series Models

Interval models based on fuzzy regression and fuzzy time-series can illustrate the possibilities of a system using the intervals in the model. Thus, the aim is to minimize the vagueness of the model in order to describe the possible states of the system. In the present study, we consider on an interval fuzzy time-series model based on a Box–Jenkins model, a fuzzy autocorrelation model proposed by Yabuuchi, and a fuzzy regressive model proposed by Ozawa. We examine two models by analyzing the Japanese national consumer price index and demonstrate that our approach improves the accuracy of predictions. The utility and predictive accuracy of fuzzy time-series models are validated using two concepts of fuzzy theory and statistics. Finally, we demonstrate the applicability of the fuzzy autocorrelation model with fuzzy confidence intervals.

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series

Peramalan Data IHSG Menggunakan Metode Backpropagation

2013 ◽  
Vol 5 (1) ◽  
pp. 26-30
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Soft Computing ◽  
Fuzzy Time Series

Jaringan saraf tiruan merupakan salah satu metode soft computing yang banyak digunakan dan diterapkan di berbagai disiplin ilmu, termasuk analisis data runtun waktu. Tujuan utama dari analisis data runtun waktu adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas dalam berbagai data runtun waktu real, termasuk data harga saham. Banyak peneliti yang telah berkontribusi dalam analisis data runtun waktu dengan menggunakan berbagai pendekatan berbeda. Chen dan Hsu, Jilani dkk., Stevenson dan Porter, dan Hansun telah menggunakan metode fuzzy time series untuk meramalkan data mendatang, sementara beberapa peneliti lainnya menggunakan metode hibrid, seperti yang dilakukan oleh Subanar dan Suhartono, Popoola dkk, Popoola, Hansun dan Subanar. Di dalam penelitian ini, penulis mencoba untuk menerapkan metode jaringan saraf tiruan backpropagation pada salah satu indikator perubahan harga saham, yakni IHSG (Indeks Harga Saham Gabungan). Penelitian dilanjutkan dengan menghitung tingkat akurasi dan kehandalan metode yang telah diterapkan pada data IHSG. Pendekatan ini diharapkan dapat menjadi salah satu cara alternatif dalam meramalkan data IHSG sebagai salah satu indikator perubahan harga saham di Indonesia. Kata kunci—jaringan saraf tiruan, backpropagation, analisis data runtun waktu, soft computing, IHSG

A Distributed Algorithm for Scalable Fuzzy Time Series

2019 ◽  
pp. 42-56 ◽  
Author(s):  
Petrônio Cândido de Lima e Silva ◽  
Patrícia de Oliveira e Lucas ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Distributed Algorithm ◽  
Fuzzy Time Series

Pooling of Time Series and Cross Section Data

Econometrica ◽  
1969 ◽  
Vol 37 (3) ◽  
pp. 552
Author(s):  
V. K. Chetty
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data
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