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

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.

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

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.

scholarly journals PREDICTING THE BITCOIN PRICE USING LINEAR REGRESSION OPTIMIZED WITH EXPONENTIAL SMOOTHING

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.

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

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. 

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

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 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.

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