scholarly journals GOLDEN EXPONENTIAL SMOOTHING: A SELF-ADJUSTED METHOD FOR IDENTIFYING OPTIMUM ALPHA

2020 ◽  
Vol 5 (2) ◽  
pp. 587
Author(s):  
Fong Yeng Foo ◽  
Azrina Suhaimi ◽  
Soo Kum Yoke
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Series Data ◽  
Training Process ◽  
Times Series ◽  
Golden Section Search ◽  
Training Stage ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Accuracy Of Prediction

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) was proposed to solve the problem. The conventional method was reformed and interposed with golden section search such that an optimum alpha which minimizes the errors of forecasting could be identified in the algorithm training process.  Numerical simulations of four sets of times series data were employed to test the efficiency of GES model. The findings show that the GES model was self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which was identified from the algorithm training stage, demonstrated good performance in the stage of Model Testing and Usage.

scholarly journals Forecasting the Number of Coronavirus (COVID-19) Cases in Ethiopia Using Exponential Smoothing Times Series Model

2020 ◽  
Author(s):  
Teshome Hailemeskel Abebe
Keyword(s):  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Smoothing Methods ◽  
Times Series ◽  
Double Exponential ◽  
Forecasting Performance ◽  
Double Exponential Smoothing ◽  
The Right ◽  
Exponential Smoothing Method

AbstractThe main objective of this study is to forecast COVID-19 case in Ethiopiausing the best-fitted model. The time series data of COVID-19 case in Ethiopia from March 14, 2020 to June 05, 2020 were used.To this end, exponential growth, single exponential smoothing method, and doubleexponential smoothing methodwere used. To evaluate the forecasting performance of the model, root mean sum of square error was used. The study showed that double exponential smoothing methods was appropriate in forecasting the future number ofCOVID-19 cases in Ethiopia as dictated by lowest value of root mean sum of square error. The forecasting model shows that the number of coronavirus cases in Ethiopia grows exponentially. The finding of the results would help the concerned stakeholders to make the right decisions based on the information given on forecasts.

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals Perancangan Aplikasi Peramalan untuk Metode Exponential Smoothing Menggunakan Aplikasi Lazarus (Studi Kasus: Data Konsumsi Listrik Kota Samarinda)

2021 ◽  
pp. 57-70
Author(s):  
Hairi Septiyanor ◽  
Syaripuddin Syaripuddin ◽  
Rito Goejantoro
Keyword(s):  
Golden Section ◽  
Electricity Consumption ◽  
Exponential Smoothing ◽  
Consumption Data ◽  
Forecasting Method ◽  
Double Exponential ◽  
Application Procedure ◽  
Golden Section Method ◽  
Smoothing Parameters ◽  
Exponential Smoothing Method

Exponential smoothing is forecasting method used to predict the future. Lazarus is an open source software based on free pascal compiler. at this research, program Lazarus be design used exponential smoothing method to predict electricity consumption data in Samarinda City from September to November 2018. Purposed of this researched is to determine the procedure of building an exponential smoothing forecasting application and obtained forecasting result using the built application. Procedure of built the application are designed interface, designed properties and filled coding. The optimum smoothing parameters were obtained used the golden section method. Based on the analysis, electricity consumption data in Samarinda City shows a trend pattern, then the forecasting was used double exponential smoohting (DES) method are DES Brown and DES Holt. The best forecasting method for at this researched is DES Holt, because DES Holt method produced MAPE 0,0659% less than DES Brown method produced MAPE 0,0843%.

scholarly journals Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

scholarly journals Optimasi Parameter Pemulusan Algoritma Brown Menggunakan Metode Golden Section Untuk Prediksi Data Tren Positif dan Negatif

2018 ◽  
Vol 2 (1) ◽  
pp. 307-314 ◽  
Author(s):  
Fiqih Akbari ◽  
Arief Setyanto ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Good Forecast

Algoritma DES (Double Exponential Smoothing) Brown merupakan algoritma peramalan yang digunakan untuk memprediksi data deret berkala baik berpola tren positif maupun tren negatif. Namun algoritma ini mempunyai kelemahan yaitu dalam menentukan nilai parameter optimum untuk meminimasi error peramalan (MAPE), nilai parameter tersebut dicari menggunakan metode Golden Section dimana sebelumnya dicari secara manual menggunakan percobaan berulang kali. Penelitian ini menggunakan 60 data berpola tren yang dianalisis untuk pengelompokan pola data tren positif dan negatif dimana selanjutnya dilakukan proses peramalan, evaluasi dan pengujian untuk mengetahui jenis pola data tren apa yang terbaik. Dari hasil perhitungan dan pengujian diketahui bahwa parameter optimasi menghasilkan nilai MAPE yang optimum, dimana selanjutnya nilai parameter tersebut dilakukan proses peramalan pada kelompok pola data tren positif dan negatif yang menghasilkan rata-rata nilai MAPE sebesar 9,73401% (highly accurate) untuk data berpola tren positif dan 15,78467% (good forecast) untuk data berpola tren negatif. Algoritma peramalan DES Brown dengan metode optimasi parameter menghasilkan nilai pendekatan terhadap data asli jika data tersebut menunjukkan penambahan atau penurunan nilai disekitar nilai rata-rata. Sebaliknya, akan menghasilkan nilai MAPE yang tinggi (tidak akurat) jika data tersebut memiliki lonjakan periode nilai data. Dari kedua kelompok nilai MAPE tersebut dilakukan uji t statistik yang menyatakan bahwa data berpola tren positif () menghasilkan nilai rata-rata MAPE lebih baik dibandingkan data berpola tren negatif (μ2).  

scholarly journals PERAMALAN GARIS KEMISKINAN DI KABUPATEN PURBALINGGA TAHUN 2021-2023 DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING LINIER SATU PARAMETER DARI BROWN

2021 ◽  
Vol 13 (2) ◽  
pp. 155
Author(s):  
Dwi Anggraeni ◽  
Sri Maryani ◽  
Suseno Ariadhy
Keyword(s):  
Time Series ◽  
Poverty Alleviation ◽  
Time Series Data ◽  
Poverty Line ◽  
Exponential Smoothing ◽  
Series Data ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
The Government ◽  
Trend Coefficient

Poverty is a major problem in a country. The Indonesian government has made various efforts to tackle the problem of poverty. The main problem faced in poverty alleviation is the large number of people living below the poverty line. Therefore, this study aims to predict the poverty line in Purbalingga Regency for the next three periods as one of the efforts that can be made by the government in poverty alleviation. The method used in this study is a one-parameter linear double exponential smoothing from Brown. The software used in this research is Zaitun Time Series and Microsoft Excel. The steps taken are determining the forecasting objectives, plotting time series data, determining the appropriate method, determining the optimum parameter value, calculating the single exponential smoothing value, calculating double exponential smoothing value, calculate the smoothing constant value, calculate the trend coefficient value and perform forecasting. Based on the calculation results, the optimum alpha parameter value is 0.7 with MAPE value of 1.67866%, which means that this forecasting model has a very good performance. The forecast value of the poverty line in Purbalingga Regency for 2021 is Rp. 396,516, in 2022 it is Rp. 417,818, and in 2023 it is Rp. 439,120.

scholarly journals PERBANDINGAN METODE SINGLE EXPONENTIAL SMOOTHING DAN MOVING AVERAGE PADA PERAMALAN PENJUALAN PRODUK MINYAK GORENG DI PT TUNAS BARU LAMPUNG

2021 ◽  
Vol 6 (2) ◽  
pp. 101
Author(s):  
Niken Chaerunnisa ◽  
Ade Momon
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Oil Products ◽  
Cooking Oil ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Product Sales ◽  
Exponential Smoothing Method ◽  
Single Exponential

PT Tunas Baru Lampung is a company that produces palm cooking oil products under the Rose Brand brand. In product sales, companies sometimes experience ups and downs. Based on the sales data from Rose Brand Cooking Oil, the size of 1 L has fluctuated or in each period it changes and is not always boarding. Even though product sales are one of the important things to be evaluated from time to time on an ongoing basis. To predict future sales, forecasting is done. The forecasting method used is Double Exponential Smoothing and Moving Average. The method of accuracy will be compared using MSE, MAD, and MAPE. The results showed a comparison of the accuracy and the smallest error value in each method. By using the weight values ​​0.1, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 on the Single Exponential Smoothing method the weight value is 0.8 or α = 0.8, namely MSE of 250,570,764.80, MAD of 12,922.32 and MAPE of 33.55 Then, using the movement value n = 3 in the Moving Average method has an accuracy of 438,980,942.75 MSE, 18,142.14 MAD, and 41.37 MAPE. After comparing the accuracy of the two methods, the Single Exponential Smoothing method is the best method to predict sales of Rose Brand 1 L Cooking Oil products.

scholarly journals FORECASTING USES DOUBLE EXPONENTIAL SMOOTHING METHOD AND FORECASTING VERIFICATION USES TRACKING SIGNAL CONTROL CHART (CASE STUDY: IHK DATA OF EAST KALIMANTAN PROVINCE)

2020 ◽  
Vol 14 (1) ◽  
pp. 013-022
Author(s):  
Humairo Dyah Puji Habsari ◽  
Ika Purnamasari ◽  
Desi Yuniarti
Keyword(s):  
Control Chart ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Signal Control ◽  
East Kalimantan ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Tracking Signal

Abstrak Peramalan merupakan suatu teknik untuk memperkirakan suatu nilai pada masa yang akan datang dengan memperhatikan data masa lalu maupun data saat ini. Data yang menunjukan suatu trend, cocok dengan metode peramalan double exponential smoothing dari Brown atau metode double exponential smoothing dari Holt. Peramalan metode double exponential smoothing pada penelitian ini diaplikasikan pada data IHK Provinsi Kalimantan Timur periode Bulan Januari Tahun 2016 hingga Bulan Februari Tahun 2019 yang berpola trend. Tujuan dari penelitian ini adalah memperoleh hasil perbandingan akurasi metode peramalan double exponential smoothing berdasarkan nilai MAPE terkecil, memperoleh hasil verifikasi metode peramalan double exponential smoothing terbaik berdasarkan grafik pengendali tracking signal, dan memperoleh hasil peramalan menggunakan metode double exponential smoothing terbaik. Hasil penelitian menunjukkan metode peramalan terbaik adalah metode double exponential smoothing dari Holt dengan parameter  dan berdasarkan nilai MAPE terkecil sebesar 0,361% dan nilai tracking signal yang keseluruhan terkendali pada grafik pengendali tracking signal.   Kata kunci: Double Exponential Smoothing, IHK, MAPE, Tracking signal.   Abstract Forecasting is a technique for estimating a value in the future by looking at past and current data. Data that shows a trend, matches the Brown’s  exponential smoothing forecasting method or Holt's double exponential smoothing method. Forecasting of double exponential smoothing method in this study was applied to the IHK data of East Kalimantan Province for the period of January 2016 to February of 2019 which has a trend pattern. The purpose of this study was to obtain the results of the accuracy comparison of the double exponential smoothing forecasting method based on the smallest MAPE value, obtain the best verification results of the double exponential smoothing forecasting method based on tracking signal control charts, and obtain the best forecasting results using the double exponential smoothing method. The results showed that the best forecasting method was Holt's double exponential smoothing method with parameters  and based on the smallest MAPE value of 0.361% and the overall tracking signal value was controlled on the tracking signal control chart.  Keywords: Double Exponential Smoothing , IHK, MAPE, Tracking signal.  

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

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