Forecasting in Fuzzy Time Series by an Extension of Simple Exponential Smoothing

2014 ◽  
pp. 257-268
Author(s):  
Fábio José Justo dos Santos ◽  
Heloisa de Arruda Camargo
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Fuzzy Time Series ◽  
Simple Exponential Smoothing

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

scholarly journals PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

2021 ◽  
Vol 10 (3) ◽  
pp. 325-336
Author(s):  
Anes Desduana Selasakmida ◽  
Tarno Tarno ◽  
Triastuti Wuryandari
Keyword(s):  
Time Series ◽  
Precious Metal ◽  
Exponential Smoothing ◽  
Fuzzy Time Series ◽  
Monthly Data ◽  
Toxic Gases ◽  
Double Exponential ◽  
Speed Up ◽  
Past Data ◽  
Double Exponential Smoothing

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 

scholarly journals Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?

2013 ◽  
Vol 17 (3) ◽  
pp. 117-126 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Error Measures ◽  
Nonlinear Trend ◽  
Simple Exponential Smoothing ◽  
The Right ◽  
Numerous Time

This paper examines exponential smoothing constants that minimize summary error measures associated with a large number of forecasts. These forecasts were made on numerous time series generated through simulation on a spreadsheet. The series varied in length and underlying nature no trend, linear trend, and nonlinear trend. Forecasts were made using simple exponential smoothing as well as exponential smoothing with trend correction and with different kinds of initial forecasts. We found that when initial forecasts were good and the nature of the underlying data did not change, smoothing constants were typically very small. Conversely, large smoothing constants indicated a change in the nature of the underlying data or the use of an inappropriate forecasting model. These results reduce the confusion about the role and right size of these constants and offer clear recommendations on how they should be discussed in classroom settings.

scholarly journals PERBANDINGAN METODE FUZZY TIME SERIES DAN HOLT DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN JUMLAH MAHASISWA BARU INSTITUT PERTANIAN BOGOR

2013 ◽  
Vol 12 (2) ◽  
pp. 25
Author(s):  
S. STEVEN ◽  
S. NURDIATI ◽  
F. BUKHARI
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Peramalan merupakan kegiatan memprediksi nilai suatu variabel di masa yang akan datang. Tujuan penelitian ini adalah memprediksi jumlah mahasiswa baru Institut Pertanian Bogor dengan menggunakan metode fuzzy time series dan metode pemulusan eksponensial ganda dari Holt serta membandingkan kedua metode tersebut dengan cara melihat tingkat ketepatan peramalan Mean Absolute Percentage Error (MAPE). Metode fuzzy time series menggunakan himpunan fuzzy dalam proses peramalannya sedangkan metode pemulusan eksponensial ganda dari Holt menggunakan pemulusan nilai dari serentetan data dengan cara menguranginya secara eksponensial. Dalam meramalkan jumlah mahasiswa baru Institut Pertanian Bogor, metode fuzzy time series menghasilkan tingkat ketepatan peramalan yang lebih baik dengan nilai MAPE sebesar 6.41 % dibandingkan dengan metode pemulusan eksponensial ganda dari Holt dengan nilai MAPE sebesar 7.75 %. Setelah dilakukan studi kasus, metode pemulusan eksponensial ganda dari Holt akan lebih akurat hasil peramalannya jika data yang digunakan lebih banyak.

scholarly journals Prediction of passenger train using fuzzy time series and percentage change methods

2021 ◽  
Vol 10 (6) ◽  
pp. 3007-3018
Author(s):  
Solikhin Solikhin ◽  
Septia Lutfi ◽  
Purnomo Purnomo ◽  
Hardiwinoto Hardiwinoto
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Rate Of Change ◽  
Smoothing Method ◽  
Fuzzy Time Series ◽  
Double Exponential ◽  
Passenger Volume ◽  
Double Exponential Smoothing ◽  
Railway Passenger ◽  
Exponential Smoothing Method

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting ◽  
Made In

ABSTRACTBackgroundThe coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. There is a lot of data since the virus started. However, these data will be explanatory when accurate analyzes are made and will allow future predictions to be made. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators.MethodsThe data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated.ResultsChina has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy.ConclusionsMore accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data.

Validation en temps réel des données des apports naturels journaliers pour la gestion des réservoirs

1998 ◽  
Vol 25 (6) ◽  
pp. 1096-1102
Author(s):  
Van-Thanh-Van Nguyen ◽  
Jean-Louis Bisson
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Estimation Method ◽  
Exponential Smoothing ◽  
Numerical Application ◽  
Smoothing Methods ◽  
Simple Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method ◽  
Simple Parameter

The main objective of this study is to propose a method for validating the daily net basin supply data to obtain the most reliable values for the management of water resource systems. Based on the regression and exponential smoothing methods, various validating procedures are developed and compared. Results of a numerical application using data from La Grande 3 basin have shown that the simple exponential smoothing method is the most suitable. Further, it has been found that the proposed exponential smoothing has the simplest structure and would require a simple parameter estimation method because it contains only one parameter.Key words: real time validation, net basin supply, forecasting, time series analysis, reservoir management.

PERBANDINGAN METODE FUZZY TIME SERIES HSU DAN DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA

2016 ◽  
Vol 1 (2) ◽  
pp. 153-162 ◽  
Author(s):  
Wulan Anggraeni
Keyword(s):  
Time Series ◽  
Error Rate ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Double Exponential ◽  
Time Series Method ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

The purpose of this study is to determine which accuracy is better between fuzzy time series method and Holt double exponential smoothing method. The data used is daily published rupiah exchange rate of Bank Indonesia in the period of 1 April 2016 until 17 june 2016. After being calculated, the error rate fuzzy time series Hsu method is at 0,6 %, while the error rate holt double exponential smoothing method is at 2,25%. Based on the calculation, it can be concluded that the error rate forecasting rupiah exchange rate using fuzzy time series method is lower than the holt double exponential smoothing It means that the fuzzy time series hsu method has better accuarcy than Holt double exponential smoothing method. The result of forecasting in 21, 22, 23, 24, and 25 respectively are Rp. 13355, Rp. 13375, Rp. 13395, Rp. 13465, Rp 13.475. Keywords: fuzzy time series, holt double exponential, forecasting

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