Holt-Winters Exponential Smoothing Method Research Articles

FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.

Forecasting COVID-19 Daily Infected Cases in Sri Lanka by Holt-Winters Exponential Smoothing Method

The novel coronavirus disease (COVID-19) has spread from China since December 2019 and spread worldwide including Sri Lanka. The aim of this study was to forecast the daily infected cases of COVID-19 in Sri Lanka which in turn help administrators for effective management of the pandemic. The method used in this study was Holt-Winters three parameter with additive or multiplicative models. The daily infected cases in Sri Lanka during the period of 22nd January 2020 to 22nd December 2021 were obtained from the publicly available databases of Epidemiology Unit of Sri Lanka and World Health Organization. The pattern recognition of the daily infected cases was examined by time series plot and Auto Correlation Function (ACF). The model validation was performed by the Anderson Darling test which confirmed the normality of residuals (p > 0.05) and ACF that confirmed the independence of residuals of the model. The forecasting ability of the model was assessed by the three measurements of errors; Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Square Error (MSE). Holt-Winters additive and multiplicative model with α (level) 0.61, β (trend) 0.4 and γ (seasonal) 0.3 at a length of repeating behaviour of 3 days, had the least relative and absolute measurement of errors during the model fitting and verification. İn the multiplicative model, MAPE, MAD and MSE were 0.2847, 0.0187 and 0.0005 respectively. Similarly in the additive model, corresponding values of MAPE, MAD and MSE were 0.0207, 0.0187 and 0.0005. The fits and the forecast of these models followed a similar pattern of the actual daily infected cases concluding that the Holt-Winters model can be used to forecast the COVID-19 outbreak in Sri Lanka.

Multidimensional analysis of losses in the number of passengers transported by air in Poland and Germany caused by the COVID-19 pandemic in terms of the maintenance of economic security

In the study, the authors conducted a multidimensional data analysis on the decrease in the number of passengers transported by air in Poland and Germany caused by the COVID-19 pandemic between 2020-2022. Forecasts of the number of passengers transported by air in Poland and Germany obtained after applying the Holt-Winters exponential smoothing method from January 2020 to December 2022 were compared with actual data. The research showed that in the case of Germany, in 2020 there was a decrease of 174 628 873 people, and in 2022, it was 90 620 354 people. In Poland, the decrease in 2020 amounted to 40 257 520 people; in 2022, it was already 21 083 066 passengers.

Analisis Model Manajemen Permintaan SCM dan Peramalan Penjualan Busana Menggunakan Metode Holt-Winter Exponential Smoothing

Sales forecasting is a highly crucial strategy in the business world, as it significantly contributes to enhancing a company's profits. In this context, sales transaction forecasting plays a vital role in assisting business decision-makers in planning effective sales strategies. The utilization of the Holt-Winters Exponential Smoothing method in sales forecasting demonstrates an effective approach. In this study, this method was applied to retail sales data of Muslim clothing from 2021 to 2023. By setting the parameters ? = 0.9, ? = 0.1, and ? = 0.1, the forecasting results indicate a high level of accuracy with low values for Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), amounting to 29.93, 295.93, and 0.62%, respectively. Consequently, the forecast reveals that the inventory of clothing for periods 13 to 16 is 83, 228, 129, and 115, respectively

Implementation of Holt-Winter Exponential Smoothing Method to Forecast The Spread of Covid-19

This research examines the Holt winters exponential smoothing method to solve forecasting problems using case studies of the spread of Covid-19. The data source uses the transmission of Covid-19 in Indonesia. MAPE is used to measure errors in data forecasting. The results are structured to serve as a recommendation for other researchers in choosing a method for predicting the spread of the disease. Based on the results, forecasting with the Holt-Winters model in positive cases produces a MAPE value of 9.21% using the Multiplicative model and the best parameter values of alpha, beta and gamma (0.4, 0.05, 0.25). Whereas in the case of human recovery, the MAPE value was 11.86% using the Multiplicative model and the best values for the parameters alpha, beta and gamma (0.4, 0.03, 0.1). And in the case of death it produces a MAPE of 17.97% using the Multiplicative model and the parameter values alpha, beta and gamma (0.4, 0.01, 0.1). So, it can be concluded that the Holt-Winters method on human positive case data shows a good outcome performance while recovered and deceased cases produce a well-being analysis performance.

Enhancing Sales Forecasting Accuracy Through Optimized Holt-Winters Exponential Smoothing with Modified Improved Particle Swarm Optimization

The Holt-Winters Exponential Smoothing method utilizes three smoothing parameters, namely alpha (α), beta (β), and gamma (γ), which have a significant impact on the accuracy of the forecasting process. One of the main challenges in the Holt-Winters Exponential Smoothing method is to find the best combination of the smoothing parameters, α, β, and γ, to achieve optimal forecasting accuracy. In this research, the MIPSO optimization method is used to find the optimal combination of values for α, β, and γ. The sales data used in the study covers the period from January 2021 to May 2023. The research results indicate the best accuracy achieved by combining the Holt-Winters Exponential Smoothing algorithm with the MIPSO optimization algorithm during the data period from January 2021 to May 2023, with a MAPE value of 9.1717%. Therefore, the use of the MIPSO algorithm helps discover the optimal combination of α, β, and γ parameters for forecasting.

Forecasting Currency in Circulation in Nigeria Using Holt-Winters Exponential Smoothing Method

Nigeria is a country located in West Africa. It is the most populous country in Africa, with a population of about 211 million. Nigeria is a federal republic with three tiers of government: federal, state, and local. Nigeria is a diverse country, with over 250 ethnic groups. This study aims to investigate the forecasting of currencies in circulation (CIC) in Nigeria using the Holt-Winters exponential smoothing method (Additive Holt–Winters Model and Multiplicative Holt–Winters Method). The forecasting data are collected from January 1960 to December 2022. The focus of the study is primarily to determine the optimal forecasting approach while considering the pertinent smoothing parameters and determine which of the forecasting method, either the multiplicative Holt–Winters or additive Holt–Winters is best in forecasting CIC in Nigeria. Furthermore, the study offers prognostic insights regarding CIC in Nigeria. Three experiments were conducted with different parameters (α= 0.2, γ= 0.2, δ= 0.5; α= 0.4, γ= 0.3, δ= 0.1; α= 0.1, γ= 0.1, δ= 0.3) that were randomly generated. The experiment with α= 0.4, γ= 0.3, and δ= 0.1 parameters was the best model for forecasting the variable using both the Additive Holt-Winters model and the Multiplicative Holt-Winters method. Based on the comparison of the accuracy measures, this study concluded that the multiplicative Holt-Winters method outperforms the additive Holt-Winters model in all three measures: MAPE, MAD, and MSD. The multiplicative Holt-Winters method has a significantly lower MAPE (5.97894E+00) compared to the additive Holt-Winters model (9.55758E+02). The forecast of CIC in Nigeria was conducted using the multiplicative Holt-Winters method, and it found that currency in circulation in Nigeria continues to increase as it shows the upper trend.

Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo

Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method.

PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.

MP75-18 WOMEN'S REPRESENTATION IN UROLOGIC ONCOLOGY PUBLISHING: 30 YEARS PAST AND FUTURE PERSPECTIVE

You have accessJournal of UrologyCME1 Apr 2023MP75-18 WOMEN'S REPRESENTATION IN UROLOGIC ONCOLOGY PUBLISHING: 30 YEARS PAST AND FUTURE PERSPECTIVE Orel Carmona, Reut Cohen, and Miki Haifler Orel CarmonaOrel Carmona More articles by this author , Reut CohenReut Cohen More articles by this author , and Miki HaiflerMiki Haifler More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003349.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urology has traditionally been a male-dominant field, with only 10.3% of practicing urologists in the US being women. Women are also underrepresented in academic publications in urology, which has a critical influence on promotion within the field of urology. The objective of this study is to examine female authorship trends in urologic oncology literature as first and last authors over the last three decades and to forecast the trend in decades to come. METHODS: A PubMed search was conducted for urologic oncology papers from January 1, 1990, to December 31, 2020. The 333 most relevant “best match” papers from each decade were extracted according to a predefined research strategy. Author gender, specialty, advanced degrees, and the continent of origin in addition to journal variables were collected. Forecasting analyses were conducted to determine the projected proportions of female authors over the next 3 decades by considering female authorship trends over the previous 3 decades, using the Holt-Winters exponential smoothing method. RESULTS: Females represent only 10% and 17% of first and last authors, with a lower H-index for females compared with males, both as first and last authors. Female authorship increased over the last 3 decades and we predict an increasing trend for female first and last authors by 2050. CONCLUSIONS: Most of urologic oncology literature has been generated by male authors. A significant rising trend was shown in female first and last authorship over the last three decades, and these trends will continue over the next 3 decades. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1087 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Orel Carmona More articles by this author Reut Cohen More articles by this author Miki Haifler More articles by this author Expand All Advertisement PDF downloadLoading ...

COMPARATIVE PERFORMANCE ANALYSIS OF ARIMA, PROPHET AND HOLT-WINTERS FORECASTING METHODS ON EUROPEAN COVID-19 DATA

COVID-19 is the most common infectious disease of the last few years and has caused an outbreak all around the world. The mortality rate, which was earlier in the hundreds, increased to thousands and then to millions. Since January 2020, several scientists attempted to understand and predict the spread of COVID-19 so that governments may make sufficient arrangements in hospitals and reduce the mortality rate. This research article presents a comparative performance analysis of ARIMA, Prophet and Holt-Winters Exponential Smoothing forecasting methods to make predictions for the COVID-19 disease epidemiology in Europe. The dataset has been collected from the World Health Organization (WHO) and includes the COVID-19 case data of European countries, which is categorized by WHO between the years of 2020 and 2022. The results indicate that Holt-Winters Exponential Smoothing method (RMSE: 0.2080, MAE: 0.1747) outperforms ARIMA and Prophet forecasting methods.

Assessment of the status and trends of photovoltaic distributed generation in Brazil: An in-depth approach based on big data processing

The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset’s average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research.

Comparison of Adaptive Holt-Winters Exponential Smoothing and Recurrent Neural Network Model for Forecasting Rainfall in Malang City

Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series.

Ensemble Forecasting Methods in DCF Modelling of the Fair Value of Enterprises

The purpose of this study is to improve the methodology for assessing the fair value of enterprises based on the DCF model with the use of ensemble methods for predicting the selling price of their products. The study compares the fair values of three Ukrainian agricultural enterprises. The value of each company was calculated in several ways: using forecast data from statistical agencies, calculated forecast values, and real indicators. The methodology is based on the use of methods and models for forecasting time series. The study uses neural networks, as well as prediction methods such as linear regression, FB Prophet model, Holt-Winters exponential smoothing method, SARIMA, XGBoost, which are combined into a single ensemble with the use of the stacking method. The study employed such general scientific methods as analysis, synthesis, abstraction, and comparison. Also, methods of graphical and tabular presentation of materials were applied. Statistical modelling was used to determine the parameters of the model. The study found that the DCF model can potentially be improved by using ensemble methods when predicting metrics such as inflation, exchange rates, and the selling price of goods and services. These metrics have a significant impact on the final result of the model and therefore the slightest changes in these input data can lead to significant deviations in the result. It was demonstrated that the use of ensemble methods only at the selling price can increase the accuracy of the model by 5-15%. It is advisable to use the results of the study in the investment activities of companies, in mergers and acquisitions of companies, as well as in measuring the fair or investment value of companies.

APPLICATION OF THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD ON THE AIR POLLUTION STANDARD INDEX IN SURABAYA

The Air Pollution Standards Index (APSI) is an indicator that shows how clean or polluted the air is in a city. It also portrays the health impacts towards the people who breathe it in. Based on the Indonesian Ministry of Environment monitoring through the Air Quality Monitoring Station (AQMS), the city of Surabaya only had 22 up to 62 days of air categorized as good in a year. The purpose of this study was to forecast APSI as a scientific-based reference for making decisions and policies that were appropriate in tackling the effects of air pollution on health. This study was non-obstructive or non-reactive research. The research method used was time series to identify the time relationship. The data used were secondary data taken from the APSI documents from 2014 to 2019 at the Surabaya City Environment Agency. The results of this study obtained the best model through α (0.8), γ (0.5), and δ (0.6) with the values of MAPE (0.104355), MAD (0.00842), and MSD (0.001050) calculated with the Holt-Winters exponential smoothing method. The highest produced forecast value of APSI was in September 2020, and the smallest was in January 2020. This study suggests the government of Surabaya to create policies and programs to suppress the number within APSI.

PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.

Comparison of the Holt-Winters Exponential Smoothing Method with ARIMA Models: Forecasting of GDP per Capita in Five Balkan Countries Members of European Union (EU) Post COVID

Comparison of the Holt-Winters Exponential Smoothing Method with ARIMA Models: Forecasting of GDP per Capita in Five Balkan Countries Members of European Union (EU) Post COVID

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

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.

Implementasi Holt-Winters Exponential Smoothing untuk Peramalan Harga Bahan Pangan di Kabupaten Pamekasan

Food is one of the primary needs that is needed by humans throughout their life. Every day humans need to eat to meet the nutritional needs of the body so that they can do their activities properly. That is why changes in food prices are always a concern for everyone. The fluctuation of food prices can be a determinant for everyone to choose what food they will consume, according to their financial condition. This study aims to forecast food prices in the future by using data on food prices in the past. With price forecasting, it can be useful for planning expenditures such as monthly shopping planning and determining the selling price of food. Price changes always occur following the influencing factors, such as changes in weather and an increase in the demand for several foodstuffs on major holidays. The method used in this research is the Holt-Winters Exponential Smoothing Method. This method is a method of forecasting, which in addition to paying attention to trend factors, also observes season factors (seasonal). This study only uses food prices in Pamekasan Regency. The data used is data on food prices for the period 2012-2019. The next forecast is carried out in the following year, namely 2020. The results show that forecasting using the Holt-Winters Exponential Smoothing Method has a good accuracy value with an average MAPE value of 1.2% for the Multiplicative Model and 1.02% for the Additive Model. This result shows that Additive Model is better than Multiplicative Model because it has a smaller MAPE value.

Research on Consumption Law Prediction of aircraft spares based on Holt-Winters

In view of the trend and seasonal factors affecting the consumption of spare parts for aviation spares, the Holt-Winters exponential smoothing method was used for prediction research. The analysis results are consistent with the actual situation, and the practice proves that the method is feasible.