First-Order Fuzzy Time Series based on Frequency Density Partitioning for Forecasting Production of Petroleum

Wulandari, Ratri; Farikhin,; Surarso, Bayu; Irawanto, Bambang

doi:10.1088/1757-899x/846/1/012063

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…the adjustment value is obtained, then the final forecast value. For the calculation of the adjusted forecast value, follow the existing rules in equation (21). For example, calculations for adjusted forecast values F ′ 2 = F 2 ± D t2 = 180:17 + 0:25 = 180:42; by doing the same way, the summary of the final forecasting results is shown in Table 7.…”

Section: Resultsmentioning

confidence: 99%

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Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning

Hariyanto

Sumanto

Khabibah

et al. 2023

Journal of Applied Mathematics

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Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy time series is a forecasting method that uses the concept of fuzzy logic, which Song and Chissom first introduced. The fuzzy time series (FTS) Markov chain uses the Markov chain in defuzzification. The determination of the length of the interval in the fuzzy time series plays an important role in forming a fuzzy logic relationship (FLR), and this FLR will be used to determine the forecasting value. One method that can be used to determine the interval length is average-based. However, several studies use partitioning based on frequency density to obtain the optimal interval length to get better forecasting accuracy. This study combines the fuzzy time series Markov chain, Average-based fuzzy time series, and Fuzzy time series based on frequency density partitioning to become average-based fuzzy time series Markov chain based on the Frequency Density Partition which conducts redivided intervals based on frequency density in the average-based fuzzy time series Markov chain method. This method is implemented in forecasting the Indonesian Islamic stock index (ISSI) for the selected period. The calculation of the accuracy level using the mean square error (MSE) and the mean average percentage error (MAPE) shows that the fuzzy Markov chain-based fuzzy time series based on the frequency density partition has a high level of accuracy in forecasting.

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Section: Resultsmentioning

confidence: 99%

“…Irawanto et al [20] used frequency density-based partitioning for stock index forecasting. Wulandari et al [21] used frequency density partitioning for forecasting the production of petroleum which resulted in a small error value.…”

Section: Introductionmentioning

confidence: 99%

Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning

Hariyanto

Sumanto

Khabibah

et al. 2023

Journal of Applied Mathematics

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“…Singh [34] reviewed prior research in the FTS field and recognized domain-specific difficulties and research trends, and attempted to classify them with implications for future study. Wulandari, Surarso [35] provided a new advancement in establishing the UoD, historical data variation, and partitioning stage. They tried to define the UoD for computing the base value to determine how many intervals should be employed.…”

Section: Guiffrida and Nagimentioning

confidence: 99%

Forecasting Forex Rate Volatility with Cutting-Edge Fuzzy Time Series Models

Bilal

Aamir

Abdullah

et al. 2023

Preprint

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The performance of fuzzy time series (FTS) prediction algorithms is impacted negatively in the presence of outlier(s), heterogeneity, or contamination in the data. As a result of these issues, standard forecasting algorithms will fail to produce reasonable forecast error rates for defuzzified outputs in understudy data. In this article, we present a robust technique for FTS by assessing how the prediction performance of the techniques is influenced by the outlier, not only to tackle this problem but also to increase forecasting accuracy. We proposed two novel robust fuzzy time series models, i.e. Trimmed Fuzzy Time Series (TFTS) and Winsorized Fuzzy Time Series (WFTS), and implemented to annual exchange rates (AERs) between the Pakistani rupee and the US dollar for comparison to other competitive models. The proposed models consider sub-partitioning in the universe of discourse, optimization of parameters method(s), and interval forecasting, which makes the forecast accuracy more precise forecasting than previously studied methods. Such forecasting techniques will assist all stakeholders, whether directly or indirectly involved, in making sensible data-driven business decisions across the country.

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“…For several decades the modification of fuzzy time series forecasting has developed rapidly to find the best method with a better error forecast. In the first basic step of defining the universe of discourse, Chen et all define how to find the range of intervals for the universe of discourse [8], still, at the same step, Jilani et al proposed dividing Chen's interval into several sub-intervals using frequency density partitions, like has been studied by B Irawanto et al with a modification of the metric approach for predicting fuzzy time series based on frequency density partitions [8]- [10]. The second basic step is to build a triangular fuzzy number using the definition of Kusuma et al [11].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Palm Oil Production Using Fuzzy Time Forecasting Two-Factor Cross Associations with Frequency Density Partitions

Wulandari

Aulia²

2022

semnasoffstat

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Economic decisions have many determining factors based on estimates of macroeconomic variables. The accuracy of decision estimates can have an important impact. Forecasting is a method to reducing uncertainty about the future, because of economic decisions have multi-factor problems, the high order fuzzy time series forecast method is more suitable than the first order fuzzy time series forecast. Predictions are made for main factors by taking influence from both factors. FLR reflects the relationship between the premise and consequence. In this paper will be discussed fuzzy time series forecasting multi-factor one order cross association based on frequency density partition as a forecasting method to forecast palm oil production with influenced by large of the area. The results of the estimates show that the proposed method has a high forecast performance, with AFER value is according to the AFER criteria table 10%, it can be concluded that the forecast has very good criteria

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First-Order Fuzzy Time Series based on Frequency Density Partitioning for Forecasting Production of Petroleum

Cited by 8 publications

References 6 publications

Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning

Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning

Forecasting Forex Rate Volatility with Cutting-Edge Fuzzy Time Series Models

Forecasting Palm Oil Production Using Fuzzy Time Forecasting Two-Factor Cross Associations with Frequency Density Partitions

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