scholarly journals Analisis Perbandingan Metode Triple Exponential Smoothing dan Metode Winter Untuk Peramalan Tingkat Hunian Hotel Aston Denpasar

2020 ◽  
Vol 3 (1) ◽  
pp. 38-50
Author(s):  
Joshua Dwi Putra Tamasoleng ◽  
Ida Bagus Ary Indra Iswara
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Parameter Method ◽  
Additive Method ◽  
Forecasting Analysis ◽  
Occupancy Rates ◽  
Error Testing ◽  
Additive Methods

Aston Denpasar Hotel is one of the five-star hotels in Bali currently requires forecasting analysis because it has constraints in determining the number of occupancy rates in the next period. The choice of method in forecasting time series largely determines the results of forecasting, so this research is conducted to find the best method of the method to be used. The method used is Triple Exponential Smoothing one parameter from brown and Winter Multiplicative and Additive methods. Based on the analysis of the best method is the Triple Exponential Smoothing one parameter method of brown with the lowest error testing with MAPE that is equal to 23.35%. While the winter multiplicative method was 28.53% and the winter additive method was 27.38%

The predicted load balancing algorithm based on the dynamic exponential smoothing

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 439-447
Author(s):  
Lijie Yan ◽  
Xudong Liu
Keyword(s):  
Time Series ◽  
Load Balancing ◽  
Exponential Smoothing ◽  
Load Prediction ◽  
The Common ◽  
Load Balancing Algorithm ◽  
Balancing Efficiency

AbstractTo a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

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 Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

scholarly journals Information technology for forecasting the financial results of insurance companies

2021 ◽  
Vol 3 (2) ◽  
pp. 87-93
Author(s):  
K. M. Berezka ◽  
◽  
O. V. Kneysler ◽  
N. Ya. Spasiv ◽  
H. M. Kulyna ◽  
...  
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Value ◽  
Insurance Companies ◽  
Rational Approach ◽  
Percentage Error ◽  
Software Module ◽  
Financial Decisions ◽  
Time Series Modelling ◽  
Financial Result

The purpose of time series modelling is to predict future indicators based on the study and analysis of past and present data. Various time series methods are used for forecasting. The article uses econometric extrapolation research methods. Analyzed scientific works are related to extrapolation methods for forecasting time series. The dynamics of the financial formation related to results of Ukrainian insurance companies by the types of their activities have been analyzed. The main factors that determine the effectiveness are determined. It was found that the most rational approach to short-term forecasting of the financial results of insurers is the use of exponential smoothing. The optimal parameters are selected for the model of exponential smoothing of the first and second order by the method on the grid. The following indicators of the quality of the model were used: the mean value of the standard deviation of the model error to the actual data, Theils coefficient of discrepancy, mean absolute percentage error MARE. The net financial result of the activities of Ukrainian insurers was predicted, the lower and upper bounds of the forecast for 2021 for a reliability level of 0.95. To predict the net financial result of the activities of Ukrainian insurers, statistical data for 10 years from 2011 to 2020 were used, the financial results of the main (insurance and other operating) activities before tax, the results of financial activities before tax, the financial results of other ordinary activities (extraordinary events) before tax, income tax. The prototype of the software module for predicting the financial performance of insurance companies was developed in Statistica and Excel. Forecasting results based on the use of econometric modelling make it possible to identify permanent positive shifts in the domestic insurance market and the activities of insurers on it; to confirm the effectiveness of the adopted strategic and tactical financial decisions of insurance companies; to increase the efficiency of insurers management based on the results of quantitative determination the degree of influence of each factor on the formation of the financial results related to their activities; to identify trends in the development of the situation in the future, to more accurately form a set of measures to maximize profits and minimize costs of insurance companies to ensure guarantees of reliable insurance protection and satisfy the interests of their owners. Keywords: financial results; insurance companies; net financial result; exponential smoothing; time series; econometric forecasting methods.

scholarly journals VISUALISASI PREDIKSI KUNJUNGAN WISATAWAN MANCANEGARA MENGGUNAKAN MODEL TIME SERIES

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 356
Author(s):  
Ruli Utami ◽  
Mohammad Whildan Indra Maulana
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Series Modeling ◽  
Model Time Series

Sektor pariwisata merupakan sektor dengan prospek tinggi untuk meningkatkan pendapatan negara melalui penerimaan Devisa. Selain itu adanya wisatawan ini juga sangat berdampak pada ekonomi kecil warga sekitar tempat wisata, sehingga sektor ini harus dikelolah dengan bijaksanan. Daya tarik wisata Indonesia sangat menarik minat dari wisatawan mancanegara untuk berkunjung ke Indoensia, hal ini harus sebanding dengan pelayanan publik yang disediakan untuk wisatawan ini; misalnya sarana dan fasilitas hotel serta layanan lain seperti imigrasi. Hal ini dapat dilakukan jika pihak yang berwenang dapat memprediksi jumlah kunjungan wisatawan masing-masing negara. Dari permasalahan tersebut diatas, maka dibuatlah sebuah aplikasi yang bertujuan untuk menampilakn visualisasi prediksi yang dihitung menggunakan pemodelan menggunakan time series modeling dengan visualisasi hasil prediksi melalui sebuah aplikasi. Dari penelitian yang telah dilakukan dengan metode exponential smoothing dapat disimpulkan bahwa nilai parameter yang paling cocok digunakan adalah nilai α = 0.6 dengan nilai MAPE 6.77%.

scholarly journals Prediction of Marine Traffic Density Using Different Time Series Model From AIS data of Port Klang and Straits of Malacca

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Akim Ramin ◽  
Masnawi Mustaffa ◽  
Shaharudin Ahmad
Keyword(s):  
Time Series ◽  
Quantitative Methods ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Traffic Density ◽  
Identification System ◽  
Ocean Engineering ◽  
Marine Traffic ◽  
Straits Of Malacca ◽  
Port Klang

In the study of ocean engineering, marine traffic is referring to the study of the pattern of the density of ships within the particular boundaries at certain periods. The Port Klang and Straits of Malacca are known for one of the heaviest traffics in Malaysia and the world. The study of traffic within this area is important, because it enables ships to avoid traffic congestion that might happen. Thus, this study is mainly aimed at   predicting or forecasting the density of the ships using the route through this waterway by using quantitative methods which are time-series models and the associative models from the Automatic Identification System (AIS) data. The moving averages, weight moving average, and exponential smoothing for the time series model and associative model have used multiple regression. The results show an exponential smoothing alpha 0.8 and give the lowest MAPE as 20.701%, thereby making this method to be the best in forecasting the future traffic density among the method categories.

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