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.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

Fuzzy Multiple-Periods Time-Series Model Based on GSP and AR for Forecasting Gold Price

2011 ◽  
Vol 211-212 ◽  
pp. 1124-1128 ◽  
Author(s):  
Jing Wei Liu ◽  
Ching Hsue Cheng ◽  
Chung Ho Su ◽  
Ming Chien Tsai
Keyword(s):  
Time Series ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Fuzzy Relations ◽  
Previous Time ◽  
Proposed Model ◽  
Comparison Results

In the recent years, traditional time series model has been widely researched. The previous time series methods can predict future problems based on historical data, but have a problem that determines subjectively the length of intervals. Song and Chissom[6-7]proposed the fuzzy time series to solve the problem of traditional time series methods. So far, many researchers have proposed different fuzzy time series models to deal with uncertain and vague data. Besides, the consideration of a forecasting stage only discusses the relations for previous period and next period. In addition, a shortcoming of previous time series models didn’t consider appropriately the weights of fuzzy relations. This study builds fuzzy rule based on association rules and compute the cardinality of each fuzzy relation. Then, calculating the weights of fuzzy relations solve above problems. Moreover, the proposed method is able to build the multiple periods fuzzy rules based on concept of large itemsets of Apriori. To verify the proposed model, the gold price datasets is employed as experimental datasets. This study compares the forecasting accuracy of proposed model with other methods, and the comparison results show that the proposed method has better performance than other methods.

scholarly journals Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases

2021 ◽  
Author(s):  
Sidong Xian ◽  
Yue Cheng
Keyword(s):  
Time Series ◽  
Fuzzy Theory ◽  
Prediction Method ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
University Of Alabama ◽  
Fuzzy C Means ◽  
Forecasting Method ◽  
Fuzzy C Means Clustering ◽  
Realistic Prediction

Abstract Time series is an extremely important branch of prediction and the research on it plays an important guiding role in production and life. To get more realistic prediction results, scholars have explored the combination of fuzzy theory and time series. Although some results have been achieved so far, there are still gaps in the combination of n-Pythagorean fuzzy sets and time series. In this paper, a pioneering n-Pythagorean fuzzy time series model (n-PFTS) and its forecasting method (n-IMWPFCM) is proposed to employ a n-Pythagorean fuzzy c-means clustering method (n-PFCM) to overcome the subjectivity of directly assigning membership and non-membership values, thus improving the accuracy of the partition the universe of discoure. A novel improved Markov prediction method is exploited to enhance the prediction accuracy of the model. The proposed prediction method is applied to the yearly University of Alabama enrollments data and the new COVID-19 cases data. The results show that compared with the traditional fuzzy time series forecasting method, the proposed method has better forecasting accuracy. Meanwhile, it has the characteristics of low computational complexity and high interpretability and demonstrates the superiority of this model from a realistic perspective.

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

2017 ◽  
Vol 09 (01) ◽  
pp. 1750001 ◽  
Author(s):  
Riswan Efendi ◽  
Mustafa Mat Deris
Keyword(s):  
Time Series ◽  
Probabilistic Models ◽  
Oil Production ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Time Series Models ◽  
Oil Consumption ◽  
Data Set ◽  
Forecasting Accuracy

Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.

scholarly journals Time Series Modeling and Forecasting of Consumer Price Index in Ghana

2019 ◽  
pp. 1-11
Author(s):  
Aabeyir Boniface ◽  
Anokye Martin
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Time Series Model ◽  
Time Series Modeling ◽  
Financial Indicators ◽  
Goods And Services ◽  
Trend Model ◽  
Modeling And Forecasting ◽  
S Model

The knowledge of economic and financial indicators is the basis of making right decisions and sound judgment with respect to investment and allocation scare of resources. Such important indicators include the consumer price index, which measures the change in the prices paid by households for goods and services consumed. A trigger in the consumer price in Ghana causes inflation which affects the purchasing power of its citizens. Knowledge of the trend of the CPI is crucial in economic planning. The study therefore sought to construct the appropriate time series model for the CPI and then use the model to predict the next nine months CPI. The study further sought to determine the type of trend model that characterizes the CPI. The Box-Jenkins methodology was adopted. The results of analysis showed SARIMA(2, 1, 1)(1, 0, 0)12 as most fitted time series model and was used to predict the consumer price index for the next nine months. The S-model was also found to be the appropriate trend model for the CPI. The SARIMA (2, 1, 1)(1, 0, 0)12 is recommended for forecasting consumer price index in Ghana.

The Generalized Fuzzy Time Series Model for Forecasting Base on the Optimization of the Length of Intervals

2014 ◽  
Vol 596 ◽  
pp. 286-291
Author(s):  
Yan Hong Wang ◽  
Wang Ren Qiu
Keyword(s):  
Time Series ◽  
Constrained Optimization ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Fuzzy Membership ◽  
Time Series Models ◽  
Mean Square ◽  
Proposed Model ◽  
Conventional Models

Based on the analysis of some conventional fuzzy time series model,this paper proposes a new method by using a single constrained optimization to determine the interval length for improving the forecasts.The conventional fuzzy time series models are enhanced by forecast-weighted method in the forecasting.In the proposed model,which is evaluated by mean square error(MSE),fuzzy membership degrees are used to calculate forecast weights. The empirical results show that the proposed model outperforms than the conventional models.

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
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