PARAMETER OPTIMIZATION OF SINGLE EXPONENTIAL SMOOTHING USING GOLDEN SECTION METHOD FOR GROCERIES FORECASTING

Department of Agriculture and Food Security Malang City, especially in the Field of Food Supply Availability and Distribution requires a reference forecasting of food prices in Malang. The method used in the forecasting calculation is Single Exponential Smoothing. In the process of calculating the Single Exponential Smoothing method, it takes alpha parameters between 0 and 1. The problem is when to estimate the alpha value between 0 to 1 with trial error with the aim of producing minimal forecasting results. Therefore, this study aims to determine the optimal alpha value. The method used in this research is the Golden Section Method. The principle of Golden Section method in this study is to reduce the boundary area so as to produce a minimum MAPE (Mean Absolute Percentage Error) value The data used in this study is the price of 9 commodities of Groceries in Malang since January 1, 2016 until December 31, 2017. The results showed that the Golden Section method found that the optimal alpha value was 0.999 with MAPE average of 9 commodities is 0.79%. So with this golden section method researchers do not need a long time to determine alpha by trial error

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Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

E3S Web of Conferences ◽

10.1051/e3sconf/201912523006 ◽

2019 ◽

Vol 125 ◽

pp. 23006

Author(s):

Dyna Marisa Khairina ◽

Aqib Muaddam ◽

Septya Maharani ◽

Heliza Rahmania

Keyword(s):

Reference Value ◽

Exponential Smoothing ◽

Smoothing Method ◽

Percentage Error ◽

Tax Revenue ◽

Regional Office ◽

Forecasting Error ◽

The Government ◽

Exponential Smoothing Method ◽

Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

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

ESTIMASI: Journal of Statistics and Its Application ◽

10.20956/ejsa.v2i2.13364 ◽

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%.

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Parameter optimization of Brown's and Holt's double exponential smoothing using golden section method for predicting Indonesian Crude Oil Price (ICP)

2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) ◽

10.1109/icitacee.2016.7892471 ◽

2016 ◽

Author(s):

Nurrahim Dwi Saputra ◽

Abdul Aziz ◽

Bambang Harjito

Keyword(s):

Crude Oil ◽

Parameter Optimization ◽

Golden Section ◽

Exponential Smoothing ◽

Oil Price ◽

Crude Oil Price ◽

Section Method ◽

Double Exponential ◽

Golden Section Method ◽

Double Exponential Smoothing

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Parameter Optimization Of Holt – Winters Exponential Smoothing Using Golden Section Method for Predicting Indonesian Car Sales

10.1109/ieit53149.2021.9587379 ◽

2021 ◽

Author(s):

Mamluatul Hani'Ah ◽

Ika Kusumaning Putri ◽

Ariadi Retno Tri Hayati Ririd

Keyword(s):

Parameter Optimization ◽

Golden Section ◽

Exponential Smoothing ◽

Section Method ◽

Car Sales ◽

Golden Section Method

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Single Exponential Smoothing Method to Predict Sales Multiple Products

INSIST ◽

10.23960/ins.v3i2.176 ◽

2018 ◽

Vol 3 (2) ◽

pp. 176

Author(s):

Rendra Gustriansyah

Keyword(s):

Prediction Error ◽

Measurement Accuracy ◽

Exponential Smoothing ◽

Smoothing Method ◽

Percentage Error ◽

Standard Measurement ◽

Multiple Products ◽

Accuracy Of Prediction ◽

Exponential Smoothing Method ◽

Single Exponential

—Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the average of percentage prediction error of products using SES is high, because MAPE value obtained is 1.056% with a smoothing parameter α = 0.9

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PEMILIHAN PARAMETER OPTIMUM MENGGUNAKAN EXPONENTIAL SMOOTHING DENGAN METODE GOLDEN SECTION UNTUK PERAMALAN JUMLAH TITIK PANAS DI KALIMANTAN TIMUR

Jambura Journal of Probability and Statistics ◽

10.34312/jjps.v2i2.10416 ◽

2021 ◽

Vol 2 (2) ◽

pp. 75-85

Author(s):

NURA WALIDA ◽

SRI WAHYUNINGSIH ◽

FDT AMIJAYA

Keyword(s):

Time Series Data ◽

Golden Section ◽

Exponential Smoothing ◽

Smoothing Parameter ◽

Smoothing Method ◽

Series Data ◽

East Kalimantan ◽

Golden Section Method ◽

Exponential Smoothing Method ◽

Parameter Values

The exponential smoothing method is one method that can be used to predict time series data by smoothing the data. In this study, the method used was exponential smoothing with one smoothing parameter from Brown. The data used is the number of hotspots in East Kalimantan from January 2019 to September 2019. The purpose of this study is to obtain the optimum smoothing parameter values for exponential smoothing from the results of the optimization process using the golden section method to minimize the MAPE value, to obtain forecasting results for each method in exponential smoothing for the number of hotspots in East Kalimantan from October to December 2019, and obtain a good exponential smoothing method to predict data on the number of hotspots in East Kalimantan. From this analysis, the researchers chose the methods used were DES and TES. The optimum smoothing parameter obtained at DES was 0,558430 and TES was 0,376352. The results of forecasting the number of hotspots obtained in DES in October were 2.142, November was 2.707, and December was 3.271 with a MAPE value of 95%. The TES method forecasting results were obtained in October as many as 2.193, November as much as 2.975, and December as many as 3.852 with a MAPE value of 108%. Based on the comparison of the MAPE values in the two methods, the DES method is better than the TES for calculating the predicted value of the number of hotspots in East Kalimantan, although the two methods are not yet suitable for handling this case.

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ON THE ACCELERATION OF OPTIMIZATION METHODS FOR THE PROBLEM OF SYNTHESIS OF MULTILAYER OPTICAL COATINGS

Journal of Automation and Information sciences ◽

10.34229/1028-0979-2021-6-2 ◽

2021 ◽

Vol 6 ◽

pp. 13-26

Author(s):

Alexander Mitsa ◽

◽

Petr Stetsyuk ◽

Alexander Levchuk ◽

Vasily Petsko ◽

...

Keyword(s):

Objective Function ◽

Golden Section ◽

Optical Coatings ◽

Generalized Gradient ◽

Arithmetic Operations ◽

Section Method ◽

Suffix Arrays ◽

Multidimensional Search ◽

Golden Section Method ◽

Speed Up

Five ways to speed up the multidimensional search in order to solve the problem of synthesis of multilayer optical coatings by using the methods of zero and first orders have been considered. The first way is to use an analytical derivative for the target quality function of the multilayer coating. It allows us to calculate accurately (within the computer arithmetic) the value of the gradient of a smooth objective function and generalized gradient of a non-smooth objective one. The first way requires the same number of arithmetic operations as well as finite-difference methods of calculating the gradient and the generalized gradient. The second way is to use a speedy finding of the objective function gradient using the prefix- and suffix-arrays in the analytical method of calculating the gradient. This technique allows us to reduce the number of arithmetic operations thrice for large-scale problems. The third way is the use of tabulating the values of trigonometric functions to calculate the characteristic matrices. This technique reduces the execution time of multiplication operations of characteristic matrices ten times depending on the computer’s specifications. For some computer architectures, this advantage is more than 140 times. The fourth method is the use of the golden section method for the one-dimensional optimization in the problems of synthesis of optical coatings. In particular, when solving one partial problem it is shown that the ternary search method requires approximately 40% more time than the golden section method. The fifth way is to use the effective implementation of multiplication of two matrices. It lies in changing the order of the second and third cycles for the well-known method of multiplying two matrices and fixing in a common variable value of the element of the first matrix. This allows us to speed up significantly the multiplication operation of two matrices. For matrices having 1000 x 1000 dimension the acceleration is from 2 to 15 times, depending on the computer's specifications.

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