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

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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Peramalan Penjualan Rebung Bambu Betung (Dendrocalamus asper) dengan Menggunakan Metode Triple Exponential Smoothing

Jurnal BETA (Biosistem dan Teknik Pertanian) ◽

10.24843/jbeta.2021.v09.i02.p04 ◽

2021 ◽

Vol 9 (2) ◽

pp. 177

Author(s):

Ni Putu Murtini ◽

I Gusti Ngurah Apriadi Aviantara ◽

Ida Bagus Putu Gunadnya

Keyword(s):

Time Series ◽

Time Series Data ◽

Exponential Smoothing ◽

Smoothing Method ◽

Series Data ◽

Bamboo Shoots ◽

Dendrocalamus Asper ◽

Fresh Products ◽

One Year ◽

Exponential Smoothing Method

ABSTRAK Rebung bambu betung (Dendrocalamus asper) merupakan salah satu olahan produk segar yang dijual di Tiara Dewata Supermarket, dimana olahan tersebut terbagi menjadi tiga yaitu rebung mentah, rebung rajang, dan rebung biasa. Masa simpan rebung tergolong sangat singkat, hanya 1 – 3 hari. Lebih lanjut, penjualan yang terjadi setiap bulan untuk ketiga produk segar ini berfluktuasi dan sulit diduga kecenderungannya. Oleh karena itu, diperlukan metode peramalan agar dapat memperkecil kerugian yang akan terjadi. Tujuan penelitian ini adalah menemukan nilai alfa terbaik yang dapat digunakan untuk memperoleh data runtun waktu peramalan yang terbaik untuk periode satu tahun mendatang terhadap ketiga jenis olahan rebung bambu betung dengan metode Triple Exponential Smoothing. Data yang digunakan pada penelitian ini yaitu data aktual penjualan ketiga olahan rebung bambu betung dari bulan Maret 2019 – Mei 2020. Nilai alfa terbaik yang dapat digunakan untuk melakukan peramalan yaitu perhitungan data runtun waktu dengan nilai alfa 0,1 – 0,9 yang memiliki nilai kesalahan (error) terkecil, dimana alfa 0,3 pada rebung mentah dengan nilai kesalahan MSE 20,146, RSME 4,488, MAPE 19%, alfa 0,4 pada rebung rajang dengan nilai kesalahan MSE 120,281, RMSE 10,967, MAPE 5%, dan alfa 0,4 pada rebung biasa dengan nilai kesalahan MSE 1306,619, RMSE 36,147, MAPE 5%. Dari perhitungan menggunakan nilai alfa tersebut dapat disimpulkan bahwa metode triple exponential smoothing valid digunakan untuk meramalkan data runtun waktu penjualan ketiga olahan rebung bambu betung dari periode Juni 2020 – Mei 2021. ABSTRACT Betung bamboo shoots (Dendrocalamus asper) is one of the processed fresh products sold at Tiara Dewata Supermarket, where the processing is divided into three, namely raw bamboo shoots, chopped bamboo shoots, and ordinary bamboo shoots. The shelf life of bamboo shoots is very short, only 1 - 3 days. Furthermore, the monthly sales for these three fresh products fluctuate and it is difficult to predict the trend. Therefore, a forecasting method is needed in order to minimize the losses that will occur. The purpose of this study was to find the best alpha value that can be used to obtain the best time series forecasting data for the next one year for the three types of Betung bamboo shoots using the Triple Exponential Smoothing method. The data used in this study is the actual sales data of the three processed bamboo bamboo shoots from March 2019 - May 2020. The best alpha value that can be used for forecasting is the calculation of time series data with an alpha value of 0.1 - 0.9 which has a value the smallest error, where alpha 0.3 in raw shoots with an error value of MSE 20.146, RSME 4.488, MAPE 19%, alpha 0.4 in chopped bamboo shoots with an error value of MSE 120.281, RMSE 10.967, MAPE 5%, and alpha 0,4 on ordinary shoots with an error value of MSE 1306,619, RMSE 36,147, MAPE 5%. From the calculation using the alpha value, it can be concluded that the triple exponential smoothing method is valid to predict the sales time series data of the three processed Betung bamboo shoots from the period June 2020 - May 2021.

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FORECASTING SEASONAL TIME SERIES DATA USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD OF ADDITIVE MODELS

Jurnal Matematika Integratif ◽

10.24198/jmi.v16.n2.29293.151-157 ◽

2020 ◽

Vol 16 (2) ◽

pp. 151

Author(s):

Nurhamidah Nurhamidah ◽

Nusyirwan Nusyirwan ◽

Ahmad Faisol

Keyword(s):

Time Series ◽

Time Series Data ◽

Additive Model ◽

Exponential Smoothing ◽

Additive Models ◽

Smoothing Method ◽

Series Data ◽

Seasonal Time Series ◽

Exponential Smoothing Method ◽

Forecasting Errors

The purpose of this study was to predict seasonal time series data using the Holt-Winters exponential smoothing additive model. The data used in this study is data on the number of passengers departing at Hasanudin Airport in 2009-2019, the source of the data obtained from the official website of the Central Statistics Agency. The results showed that the Holt-Winters exponential smoothing method on the passenger's number at Hasanudin Airport in 2009 to 2019 contained trend patterns and seasonal patterns, by first determining the initial values and smoothing parameters that could minimize forecasting errors.

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PENGEMBANGAN ESTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING HOLT-WINTERS ADDITIVE MENGGUNAKAN METODE OPTIMASI GOLDEN SECTION (Studi Kasus: Wisatawan Mancanegara yang Menggunakan Jasa Akomodasi di DIY)

Jurnal Gaussian ◽

10.14710/j.gauss.v7i4.28861 ◽

2018 ◽

Vol 7 (4) ◽

pp. 348-360

Author(s):

Muhammad Aqajahs Al Qarani ◽

Rukun Santoso ◽

Diah Safitri

Keyword(s):

Time Series Data ◽

Forecast Error ◽

Golden Section ◽

Optimization Method ◽

Exponential Smoothing ◽

Series Data ◽

Seasonal Patterns ◽

Optimum Parameters ◽

Golden Section Method ◽

Selection Of

Forecasting is an activity to estimate what will happen in the future, one method that can be used is Exponential Smoothing. In this study used the smoothing method of Exponential Smoothing Holt-Winters Additive with three parameters that can be used for prediction of time series data that has trend patterns and seasonal patterns. The problem that arises in this method is to determine the optimum parameter to minimize the forecast error value. This study uses the Golden Section optimization method to estimate the optimum parameters that minimize the MAPE value. The data used is data on foreign tourists who use accommodation services in Yogyakarta from the period January 2009 to December 2016 that have trend patterns and additive seasonal patterns. In simplifying the optimization calculation process, a syntax using RStudio is arranged which contains the Golden Section algorithm to determine the combination that has the optimum parameters. In this optimization there are two treshold error, namely 0.001 and 0.00001. The results showed that the parameter estimator with the Golden Section method for the treshold error of 0.001 obtained MAPE of 18,96732% and for treshold error of 0.00001 MAPE was 18,96536%. This value is in the same MAPE criteria which is 10% ─ 20% (good) so that the selection of the best model is determined based on minimal iteration. Therefore the weighting parameter value used is the result of optimization with ε ≤ 0.001, then from the selected model it is used to predict the number of foreign tourists using accommodation services in Yogyakarta in the next 12 months.

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

10.1101/2020.06.29.20142489 ◽

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.

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

ZERO Jurnal Sains Matematika dan Terapan ◽

10.30829/zero.v4i2.3438 ◽

2019 ◽

Vol 4 (2) ◽

Author(s):

Vivi Aida Fitria

Keyword(s):

Golden Section ◽

Exponential Smoothing ◽

Food Prices ◽

Percentage Error ◽

Boundary Area ◽

Section Method ◽

Golden Section Method ◽

Long Time ◽

Exponential Smoothing Method ◽

Single Exponential

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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PREDICTING THE BITCOIN PRICE USING LINEAR REGRESSION OPTIMIZED WITH EXPONENTIAL SMOOTHING

Jurnal Riset Informatika ◽

10.34288/jri.v3i3.238 ◽

2021 ◽

Vol 3 (3) ◽

pp. 277-282

Author(s):

Indah Suryani ◽

Hani Harafani

Keyword(s):

Time Series ◽

Linear Regression ◽

Time Series Data ◽

Exponential Smoothing ◽

Series Data ◽

Linear Regression Method ◽

Economic Uncertainty ◽

Transformation Technique ◽

Method Optimization ◽

Exponential Smoothing Method

Bitcoin is one of the most popular cryptocurrencies today. In the current pandemic conditions that hit the world due to Covid-19, bitcoin is expected to be used as an investment when the level of economic uncertainty is high. In this study, the data used is bitcoin price data which is included in time series data. One of the commonly used methods for prediction in time series is the linear regression method. And to be able to develop the prediction results, a data transformation technique is used using the popular method, namely exponential smoothing. In the exponential smoothing method, optimization of the alpha parameter is carried out to be able to boost the prediction results from linear regression. And from the experimental results, it is evident that the optimization of the alpha parameter in exponential smoothing is able to improve the prediction performance of linear regression with the results of the comparison of RMSE with the t test which has resulted in significant differences.

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

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol14iss1pp013-022 ◽

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.

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Application of Self-Adaptive Exponential Smoothing Method in the Water Quality Forecast of Poyang Lake

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.1464 ◽

2012 ◽

Vol 518-523 ◽

pp. 1464-1467

Author(s):

Bin Xiang Liu ◽

Qun Cao ◽

Xiang Cheng

Keyword(s):

Water Quality ◽

Dynamic Characteristic ◽

Poyang Lake ◽

Exponential Smoothing ◽

Smoothing Parameter ◽

Smoothing Method ◽

Forecast Result ◽

Exponential Smoothing Method ◽

Water Quality Forecast ◽

Self Adaptive

The smoothing parameter is a constant when forecasting water quality using exponential smoothing, which usually renders the error to be enlarged, but the assumption of constant is out of accord with the practice. Based on the deep analysis of deficiency of traditional exponential smoothing, this paper establishes self-adaptive exponential smoothing model and compares the forecast result. It is proved that the dynamic characteristic of water quality can be better reflected and the forecasting precision can be improved further by self-adaptive exponential smoothing model.

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