Forecasting Total Population Using Chen, Cheng, and Markov Chain Fuzzy Time Series Models

Bettiza, Martaleli

doi:10.1109/icitee49829.2020.9271682

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Kafi et al (2019) predicted the closing price of Company X shares using the Markov Chain method and (Novitasari et al, 2019) predicted the number of sunspots to identify flares using the Markov Chain method. In 2020, (Alyousifi et al, 2020) predicted the daily basis air pollution index in Malaysia using the Markov Chain method, (Arnita et al, 2020) predicted rainfall in Medan using Chen, Cheng, and Markov Chain method, (Bettiza, 2020) predicted Indonesia's total population using Chen, Cheng and Markov Chain method, (Ramadani and Devianto, 2020) predicted bitcoin prices using Chen and Markov Chain methods and (Taufik et al, 2020), predicted gold prices in Indonesia using Markov Chain method. Ariyanto et al (2021) predicted the sales at PT Y as an improving method to get the cheapest storage cost possible using the Chen method, (Boechel et al, 2021), predicted the apple tree phenological stages using the Chen method, (Rizqon and Jati, 2021), predicted train passengers' numbers in Indonesia using Chen method and (Wirawan et al, 2021), predicted the chili prices in Indonesia using Chen and Cheng methods.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Truck Demand Computational Model

Mauludin,

Utama

2023

Journal of Computer Science

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Trucking is one of the essential aspects linking suppliers and customers in many countries. Services at a trucking service provider will be based on a first-come-first-served basis. When a customer contacts a trucking service provider, there is a possibility that all the truck units owned are in fact being used to serve other customers causing potential delays in their delivery arrangement. For the trucking service provider, the truck demand prediction is not well observed resulting in a loss of business opportunities. The purpose of this research is to develop a truck demand prediction model using the Chen Fuzzy Time Series (FTS) method. The dataset used in this research was 11 years of monthly trucking demand information from a trucking service provider in Batam City, Indonesia. The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 4.4%, this result indicates that the prediction was very accurate. After model deployment, expected can improve the readiness of trucking units.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Truck Demand Computational Model

Mauludin,

Utama

2023

Journal of Computer Science

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“…Cheng FTS has been widely used in forecasting problems and produces good forecasting result with minimal error. The model has been used to predict the total population of Indonesia (Bettiza et al, 2020) and the forecast result shows a very high accuracy with Mean Average Percentage Error (MAPE) of 0.0535%. Besides that, the model has been applied to forecast the number of tourists visiting the Province of West Sumatra (Rahmawati, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Cheng Fuzzy Time Series Model to Forecast the Price of Crude Oil in Malaysia

Bidin¹,

Sharif

Abas

et al. 2022

JCRINN

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Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded. Therefore, major price changes on the commodity have a significant influence on world economy. Market sentiment, demand, and supply are some elements directly influencing the oil prices. Since crude oil is the backbone of businesses and is extremely important to the economy, it is essential to study the price of crude oil for future planning purposes. For that reason, this study proposes the use of the Fuzzy Time Series Cheng to predict crude oil price in Malaysia. In this study, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the forecast performance. The result shows that Fuzzy Time Series Cheng is able to produce a good result in forecasting since the analyses shows that the low value of RMSE and MAPE (less than 10 percent). Although this is the fundamental study but the finding may assist many sectors in Malaysia, such as governments, enterprises, investors, and businesses to produce a better economic planning in the future especially after the pandemic covid-19 phase.

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Forecasting Total Population Using Chen, Cheng, and Markov Chain Fuzzy Time Series Models

Cited by 2 publications

References 15 publications

Fuzzy Logic-Based Truck Demand Computational Model

Fuzzy Logic-Based Truck Demand Computational Model

Cheng Fuzzy Time Series Model to Forecast the Price of Crude Oil in Malaysia

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