Application of Weighted Fuzzy Time Series Model to Forecast Trans Jogja’s Passengers

A’yun, Kurrotul; Abadi, Agus Maman; Saptaningtyas, Fitriana Yuli

doi:10.17706/ijapm.2015.5.2.76-85

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…In a solar forecasting problem the use of Fuzzy Time Series (FTS) provides significantly better results than other approaches in the literature [13]. Based on Weighted Fuzzy Time Series (WFTS), K. A'yun,et al [14] predict Trans Jogja passenger and stated that forecasting process with WFTS model is better than forecast with fuzzy time series model. FTS Markov chain (FTSMC) model urged Mahmod Othman, et al [15] to predict a daily air pollution index model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution and stated that the proposed forecasting method has produced a higher prediction accuracy as compared to some FTS models.…”

Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

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Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Moneim

2020

CJAST

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Aims: It is important to predict the amount of COVID-19 injuries. Since the first suspected case of novel coronavirus (2019-nCoV) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the large amounts of daily publicly available epidemiological data and the need to make an accurate prediction of future behavior requires the definition of powerful and effective techniques capable of inferring random dependency between the past and the future from observations. In this paper, we apply a rewarding model to predict injuries in areas where COVID-19 is, especially in the Arab region. This forecast uses epidemic injuries data from March 2nd, 2020 to July 20th, 2020 in Saudi Arabia. Methodology: We propose the use of weighted fuzzy time series techniques (WFTS) and weighted non-stationary fuzzy time series techniques (WNSFTS) to be compared with the classical Auto-Regressive Integrated Moving Average (ARIMA) statistical method. The available data is not a stationary and should therefore be converted first to stationary to forecast it with (ARIMA) and (WFTS) techniques. We do a log transform and differencing on our injuries dataset. Results: When we examine the original data by Dickey-Fuller Test (DFT) to get p-value, we find it is equal to 0.646, it is more than 0.05 which implies the non-stationarity. The mean square error (MSE), the root mean square error (RMSE) and normalization root mean square error (NRMSE), are applied to compare the accuracy of the methods. The results show that WFTS methods give good services for predicting epidemic injuries in the territory by COVID-19. Conclusion: The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) in forecasting epidemic injuries problem can provide significantly better results because it is able to predict the infected cases at the next time and achieve great predictive accuracy.

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Section: Fuzzy Time Series Forecastingmentioning

confidence: 99%

“…The forecasting process of injuries with stationary data that has been obtained is conducted by the steps as stated in [13] and [14]. Fuzzy time series (FTS) has a different representation of a time series.…”

Section: Forecasting Using Fuzzy Time Seriesmentioning

confidence: 99%

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Moneim

2020

CJAST

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“…This study uses the dataset of the performance of diesel power plant feeder Keledang, East Kalimantan -Indonesia in 2016 as shown in Table 5. The average data calculation is using the following formula: (10) For standard deviation calculation is using the following formula: (11) Referring to Table 1, it can be seen that the average data is smaller than the standard deviation. The high deviation of data distribution can lead to significant prediction errors if existing data is used as predictive historical data.…”

Section: Datasetmentioning

confidence: 99%

“…There are many methods that can be used to perform predictive activities, both based on statistical methods and machine learning methods. One of them is by using First order Fuzzy Time Series (1 st order FTS) as it has been done in [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Diesel Fired Power Plant Feeder Performance using First Order Fuzzy Time Series

2019

IJITEE

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Power outages caused by factors outside the established policy will have an impact on the decline in electricity supply services and other cost related impacts. The reliability of the power plant feeder, in this case, is very important to monitor and maintain. The performance of power plant feeder can be reviewed based on the variable duration of power outage and power which fails to distribute. In this study, 1st order FTS (Fuzzy Time Series) is used to predict the feeder's performance through the predictive activity of both those variables in the actual year and the following year. The prediction results state that in 2017 there was a 20.54% decrease in performance

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“…Langkah-langkah dan hasil dari peramalan menggunakan metode WFITS Lee orde pertama dapat dilihat sebagai berikut : No A2, A2, A4, A4, A4 A3, A4, A3, A4, A5, A2 A6, A5, A4, A5, A4, A4, A4, A4, A5, A3, A5, A4, A5, A5, A4, A5, A6, A5, A4, A4, A4, A4, A4 A6, A6, A3, A4, A4, A6, A4, A6, A5, A2, A4, A5, A8, A5, A6, A5, A6, A6, A3, A4 A8, A5, A4, A4, A5, A5, A6, A1, A5, A5, A5 (1,2) No…”

Section: Metode Wfits-lee Orde Pertamaunclassified

PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

Rahmawan

Safitri

Widiharih

2019

J.Gauss

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Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

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Application of Weighted Fuzzy Time Series Model to Forecast Trans Jogja’s Passengers

Cited by 6 publications

References 15 publications

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

Prediction of Diesel Fired Power Plant Feeder Performance using First Order Fuzzy Time Series

PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

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