scholarly journals Time series of mathematics education program, FKIP university of jember enthusiast through exponential smoothing method

2020 ◽  
Vol 1465 ◽  
pp. 012017
Author(s):  
S Setiawani ◽  
A Fatahillah ◽  
P Lailani
Keyword(s):  
Time Series ◽  
Mathematics Education ◽  
Education Program ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Exponential Smoothing Method

scholarly journals PENERAPAN METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN JUMLAH KLAIM DI BPJS KESEHATAN PAMEKASAN

2016 ◽  
Vol 2 (1) ◽  
pp. 46 ◽  
Author(s):  
Faisol Faisol ◽  
Sitti Aisah
Keyword(s):  
Time Series ◽  
Average Method ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Smoothing Method ◽  
Repair Procedure ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Time series model is the model used to predict the future using past data, one example of a time series model is exponential smoothing. Exponential smoothing method is a repair procedure performed continuously at forecasting the most recent data. In this study the exponential smoothing method is applied to predict the number of claims in the health BPJS Pamekasan using data from the period January 2014 to December 2015, the measures used to obtain the output of this research there are four stages, namely 1) the identification of data, 2) Modeling, 3) forecasting, 4) Evaluation of forecasting results with RMSE and MAPE. Based on the research methodology, the result for the period 25 = 833.828, the 26 = 800.256, period 27 = 766.684, a period of 28 = 733.113, period 29 = 699.541, and the period of 30 = 655, 970. Value for RMSE = 98.865 and MAPE = 7.002, In this case the moving average method is also used to compare the results of forecasting with double exponential smoothing method. Forecasting results for the period 25 = 899.208, the 26 = 885, 792, 27 = 872.375 period, a period of 28 = 858.958, period 29 = 845.542, and the period of 30 = 832.125. Value for RMSE = 101.131 and MAPE = 7.756. Both methods together - both have very good performance because the value of MAPE is below 10%, but the method of exponential smoothing has a value of RMSE and MAPE are smaller than the moving average method.

scholarly journals Forecasting Migration Flows for New Zealand: Arrivals of New and Returning New Zealanders & Departing by Destinations

1970 ◽  
Author(s):  
Xintao Zhao ◽  
Ram SriRamaratnam ◽  
Dirk Van Seventer
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Linear Time ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Time Series Regression ◽  
Quarterly Data ◽  
Migration Flows ◽  
Exponential Smoothing Method ◽  
New Zealanders

The purpose of this paper was to outline the methods and to report results of an econometric attempt to forecast New Zealand migration flows. Flows were decomposed into eight components: two relating to arrivals and six components relating to departures by several destinations. Linear time series regression and the Holt­Winters exponential smoothing method were applied to quarterly data from June 1978 to June 2008 or from March 1990 to June 2008. Within­sample mean absolute percentage errors were presented and full­sample estimates from June 1978 to September 2010 or from March 1990 to September 2010 were used to forecast migration flows for each component for the next two years.

FORECASTING THE DEVELOPMENT OF SOCIALLY SIGNIFICANT DISEASES IN THE REGION BASED ON EXPONENTIAL SMOOTHING OF TIME SERIES

2020 ◽  
pp. 114-118
Author(s):  
Евгений Николаевич Коровин ◽  
Алина Николаевна Ненашева ◽  
Маргарита Анатольевна Сергеева
Keyword(s):  
Time Series ◽  
Mental Disorders ◽  
Action Plan ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Malignant Neoplasms ◽  
General Increase ◽  
The Future ◽  
Future Situation ◽  
Exponential Smoothing Method

В данной статье рассматривается один из подходов прогнозирования и анализа развития социально значимых заболеваний в Тамбовской области с использованием метода экспоненциального сглаживания. Анализ социально значимых заболеваний является одной из важных задач, стоящих перед медициной. С помощью прогнозирования можно разработать план мероприятий по уменьшению уровня развития социально значимых заболеваний. Целью статьи является составления прогноза на 2020-2022 года на основе статистики заболеваемости в период с 2008 по 2019 год для изучения будущей ситуации по развитиюзаболеваний в Тамбовской области. В работе был использован метод экспоненциального сглаживания, так как данный метод является одним из распространённых в прогнозировании, дающий достаточно точный общий прогноз. Результаты прогнозирования были визуализированы в виде графиков и проанализированы. По итогу анализа было выявлено будущее понижение уровня заболеваемости туберкулезом и злокачественными новообразованиями, повышение уровня заболеваемости психическими расстройствами, а также выявлено общее повышение заболеваемости социально значимыми заболеваниями в Тамбовской области. Данное прогнозирование позволит медицинским организациям регионаразработать дальнейшие мероприятия по снижению заболеваемости с учетом динамики роста или уменьшения уровня развития социально значимых заболеваний This article discusses one of the approaches to predicting and analyzing the development of socially significant diseases in the Tambov region using the exponential smoothing method. The analysis of socially significant diseases is one of the important tasks facing medicine. With the help of forecasting, you can develop an action plan to reduce the level of development of socially significant diseases. The purpose of the article is to make a forecast for 2020-2022 based on morbidity statistics in the period from 2008 to 2019 to study the future situation in the development of diseases in the Tambov region. The method of exponential smoothing was used in the work, since this method is one of the most widespread in forecasting, which gives an accurate general forecast. The results of the forecast were visualized in the form of graphs and analyzed. The analysis revealed a future decrease in the incidence of tuberculosis and malignant neoplasms, an increase in the incidence of mental disorders, as well as a general increase in the incidence of socially significant diseases in the Tambov region. This forecasting will allow medical organizations in the region to develop further measures to reduce morbidity, taking into account the dynamics of growth or decrease in the level of development of socially significant diseases

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 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 PERBANDINGAN PERAMALAN HASIL PRODUKSI IKAN MENGGUNAKAN METODE PERMULUSAN EKSPONENSIAL HOLT-WINTERS DAN ARIMA

2018 ◽  
Vol 7 (4) ◽  
pp. 371
Author(s):  
DIAN RAHMAN ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Time Series ◽  
Fish Consumption ◽  
Exponential Smoothing ◽  
Time Series Forecasting ◽  
Smoothing Method ◽  
Fish Production ◽  
Economic Activities ◽  
Seasonal Factors ◽  
Exponential Smoothing Method

Every year fish consumption in Indonesia always increases. Basis landing of fish (PPI) Kedonganan is one of the ports where the ships docked and take part in economic activities of fisheries in Bali. The aim of this study is to forecast the amount of fish production so that the fish production can be managed. Some methods that can handle seasonal factors in time series forecasting include Holt-Winters exponential smoothing and ARIMA. The results show that ARIMA(2,0,4)×(3,1,2)12 yields RMSE is 89,358 and MAPE is 0.81% whereas the value of fish outcomes prediction using Holt-Winters exponential smoothing method yields RMSE is 119,158 and MAPE is 1.14%.

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