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.

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.

scholarly journals Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

2020 ◽  
Author(s):  
Prashant Verma ◽  
Mukti Khetan ◽  
Shikha Dwivedi ◽  
Shweta Dixit
Keyword(s):  
Time Series ◽  
Health Care Systems ◽  
Medical Personnel ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Time Series Methodology ◽  
Near Future

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

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.

INTER-QUARTILE RANGE APPROACH TO LENGTH–INTERVAL ADJUSTMENT OF ENROLLMENT DATA IN FUZZY TIME SERIES FORECASTING

2013 ◽  
Vol 12 (03) ◽  
pp. 1350016 ◽  
Author(s):  
ZUHAIMY ISMAIL ◽  
RISWAN EFENDI ◽  
MUSTAFA MAT DERIS
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Forecasting Accuracy ◽  
Inter Quartile Range ◽  
Partition Number ◽  
Quartile Range ◽  
High Level ◽  
Enrollment Data ◽  
Universe Of Discourse

Various methods have been presented to investigate the length of data interval and partition number of universe of discourse in fuzzy time series to achieve high level forecasting accuracy. However, the interval length is still an issue and thus, influencing the forecasting accuracy. This paper proposes a new approach using the average inter-quartile range to improve the interval length and subsequently the partition number of universe of discourse. Moreover, in forecasting method, the first-differencing data is also considered to obtain better forecast. The enrollment data of Alabama University is used as an example and the efficiency of the proposed method is compared with the previous methods. The result shows that the proposed method improves the accuracy and efficiency of the yearly enrollment forecasting opportunities.

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.

Model Grey (1,1) dan Grey-Markov pada Peramalan Realisasi Penerimaan Negara

2018 ◽  
Vol 7 (1) ◽  
pp. 1-12
Author(s):  
Atina Ahdika
Keyword(s):  
Time Series ◽  
Markov Model ◽  
National Development ◽  
Time Series Model ◽  
Time Series Models ◽  
Minimal Amount ◽  
Forecasting Accuracy ◽  
Chain Analysis ◽  
Minimal Data ◽  
State Revenues

Setiap Negara memerlukan sumber penerimaan untuk mewujudkan pembangunan nasional dan membiayai segala keperluannya. Di Indonesia, terdapat tiga sumber utama penerimaan Negara; penerimaan pajak, penerimaan bukan pajak, serta penerimaan hibah baik dari dalam maupun luar negeri. Untuk mengantisipasi berbagai keperluan serta mengoptimalkan penggunaan penerimaan Negara, maka perlu adanya proyeksi realisasi penerimaan Negara dari ketiga sumber tersebut. Proyeksi tersebut dapat dilakukan dengan menggunakan model deret waktu klasik baik model deterministik maupun model stokastik. Namun demikian, pada model deret waktu klasik terdapat beberapa asumsi yang harus dipenuhi seperti pola data atau jumlah minimal data. Sebagai alternatif, pada penelitian ini akan dilakukan peramalan dengan menggunakan model Grey (1,1) dan Grey-Markov; perpaduan antara model Grey dengan analisis Rantai Markov. Model ini memiliki keunggulan dibandingkan model deret waktu klasik, yaitu tidak perlu adanya asumsi mengenai pola data serta peramalan dapat dilakukan meskipun data yang dimiliki cukup kecil (minimal 4 data). Hasil analisis menunjukkan bahwa model Grey-Markov secara umum memberikan akurasi peramalan yang lebih baik dibandingkan dengan model Grey (1,1). [Every state requires a source of revenue to realize its national development and fund its needs. In Indonesia, there are three main sources of state revenues; tax, non-tax, and grant revenues both from within and outside the country. To anticipate various purposes and optimize the use of state revenues, it is necessary to project the realization of the revenues from these three sources. The projection can be done using a classical time series model in both deterministic and stochastic models. However, in the classical time series model there are several assumptions that must be met such as data patterns or minimal amount of data. Alternatively, in this study the data will be forecasted using Grey (1,1) model and Grey-Markov model; a combination of the Grey model with Markov Chain analysis. This models have advantages over the classical time series models, ie no need for assumption about data pattern and forecasting can be done even though there are small size data (at least 4 data). The results of the analysis show that generally Grey-Markov model provides better forecasting accuracy compared with Grey (1,1) model.]

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

2018 ◽  
Vol 26 (05) ◽  
pp. 855-873 ◽  
Author(s):  
Riswan Efendi ◽  
Mustafa Mat Deris
Keyword(s):  
Time Series ◽  
Fuzzy Model ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Fuzzy Function ◽  
Car Accidents ◽  
Proposed Model ◽  
Enrollment Data

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and noninverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules.

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