Brown's Double Exponential Smoothing Research Articles

Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.

Double Exponential Smoothing Method to Forecast the Production of Tembakau in North Sumatra

Forecasting is the process of using mathematical calculations and quantitative data from the past to predict situations that have not occurred or will occur in the future. Determine the strategy used in pursuing choices based on several things that have been done so that it is very feasible for planning. Brown's Double Exponential Smoothing, especially the smoothing approach with alpha values of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 will be used as a forecasting technique in this study. Using tobacco production data from 2012 to 2021, the forecasting results show that the best guess value achieves the smallest Mean Absolute Precentage Error (MAPE) of the nine alpha values used. with the parameter alpha = 0.2 produces the lowest error rate of 21.37 percent. The form of the forecasting equation is Fi+m = 1666.84892857549 + 86.604473843712 m. That is, the accuracy of forecasting the amount of tobacco production in the next ten years is 65.37 percent.

PERAMALAN PENJUALAN BERAS DI PERUM BULOG SUB DIVRE MEDAN MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING

Perum Bulog as a State-Owned Enterprise has the main task, which is to conduct a quality and adequate basic food logistics business for the survival of the people. The problem that occurred in Perum Bulog Sub Divre Medan is that the rice supply in Bulog does not consider the demand in the market. Forecasting is an important tool in effective and efficient planning. Therefore, prediction is indispensable for predicting future events. This method essentially uses past data initiated by performing an exponentially decreasing weighting of older observational values or newer values. Brown's double exponential smoothing is a linear model proposed by Brown. This double exponential smoothing method is used when the data indicate a trend. In this study, the terbaik best parameter for forecasting the Number of Rice Sales in Perum Bulog Sub Divre Medan was α = 0.2 with MAPE of 0.27%. And the results of the forecast for Rice Sales at Perum Bulog Sub Divre Medan in 2022 are decreasing every month.

Peramalan Curah Hujan dengan Pengelompokan Bulan Menggunakan Metode Double Exponential Smoothing dari Brown

It is important to know the future rainfall, the way is to predict rainfall. By knowing future rainfall, development strategies such as irrigation, dams, urban drainage, harbour docks, agriculture, and even disaster mitigation strategies will be right on target. The purpose of this study is to predict the rainfall for Boyolali Regency and Surakarta City, Central Java in 2020-2021 with Brown's double exponential smoothing method. The type of data used is secondary data, which is obtained from the Meteorology, Climatology and Geophysics Agency of Semarang Climatology Station. The smoothing value, alpha, used in each month was varies, because the rainfall data is grouped first based on the same month, before it was forecasted. The results showed that the highest rainfall in 2020 was 490.73 mm (April) and the lowest was 3.25 mm (August). While for 2021, the highest rainfall was 521.37 (April) and the lowest was 3.25 mm (August), with an MSE of 14731.56, RMSE of 121.37, MAD of 97.42, and SSE of 176778.69.

Comparison of Exponential Moving Average and Brown's Double Exponential Smoothing Method for Forecasting Glass Craft Sales

Glass crafts are one of the MSME products in Indonesia, in selling process transactions there’re many inconsistent between the available stock and the agreed transactions number. This will have a significant impact on customer trust which will lead to a reduction purchases number. So the researchers took the initiative to make sales forecasts to predict next sales number, due to the many forecasting methods available, the researchers will use two methods which will be compared to find out the most effective method for forecasting sales number. From the forecasting calculations using the exponential moving average method and the Brown's double exponential smoothing method, it can be concluded that the best method used in calculating sales forecasting for glass craft products at MSMEs is the Brown's Double Exponential Smoothing method using α value of =0.5 which produces MAPE is 3.19% and the next sales forecast value (January 2021) is 6.33.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

PEMODELAN METODE BROWN’S DOUBLE EXPONENTIAL SMOOTHING (B-DES) DAN BROWN’S WEIGHTED EXPONENTIAL MOVING AVERAGE (B-WEMA) MENGGUNAKAN OPTIMASI LEVENBERG-MARQUARDT PADA JUMLAH WISATAWAN DI JAWA TENGAH

The tourism sector is one of the national development priority sectors because it contributes to foreign exchange earnings, the development of business areas, and the absorption of investment and labor. In 2018 the tourism sector will become the second largest foreign exchange earner after oil palm. Foreign exchange contributed by the tourism sector in 2018 was US $ 19.29 billion, an increase of 15.4%. The increase in contributions was driven by an increase in the number of foreign tourist arrivals by 12.58%, domestic tourists by 12.37%, and from investment. Therefore it is necessary to study the forecasting of the number of tourists after seeing the great potential generated from the tourism sector. The data forecast is data on the number of tourists in Central Java, both foreign and domestic data. Both data shows the tendency of an upward trend pattern. So that both data can be analyzed using B-DESmethods (Brown's Double Exponential Smoothing) and B-WEMA (Brown's Weighted Exponential Moving Average)that are optimized with LM (Levenberg-Marquardt). Both methods are able to analyze trend patterned data without assumptions making it easier in the analysis process. In addition, the two methods in previous studies were able to produce a small forecasting accuracy. The MAPE (Mean Absolute Percentage Error) value out sample is used to compare the forecasting results of the two methods. The results of the implementation of LM optimization on the data of the number of domestic tourists obtained the optimal parameter value of the B-DES method is 0.21944386 with MAPE out sample 16.26516% and B-WEMA method is 0.219441 with MAPE out sample 16.26515%. While the data on the number of foreign tourists obtained the optimal parameter value of the B-DES method was 0.26213368 with the MAPE out of the sample 23.61278% and the B-WEMA method was 0.26213367 with the MAPE out the sample 23.61278%. This means that both methods have a good level of forecasting accuracy in the data on the number of domestic tourists and an adequate level of accuracy in the data on the number of foreign tourists. Keywords : B-DES, B-WEMA, Levenberg-Marquardt, Tourists in Central Java

Peramalan Rate of Return Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt

Stocks can be defined as a sign of the participation of unilateral capital in a company or a limited liability company in the form of proof of a company's assets and it is formed as a valueble proof letters as a statement of participating in capital. Return stockis one of the factors that motivates investors to invest or interact and also a reward for the courage of investors to take risks or investments. PT Bank Rakyat Indonesia is one of the largest banks in Indonesia that experiences an increase in stock prices every year. The Brown's Weighted Exponential Moving Average (BWEMA) method which is a combination of the Weighted Exponential Moving Average (WEMA) and Brown's Double Exponential Smoothing (BDES) methods will be used in this study. The data used is the daily data of closing price of the stock (closing price) of PT. Bank Rakyat Indonesia on February 6, 2018 until February 6, 2019. MSE and MAPE BWEMA values ​​were obtained at 6124.222 and 1.831685%, while MSE and MAPE WEMA values ​​were 7559.211 and 1.998439% respectively. The results obtained show that the BWEMA method has smaller MSE and MAPE values. This shows that the BWEMA method is better than the WEMA method in terms of forecasting. As a results the BWEMA method is continued to calculate the forecasts rate of return for the next 7 days. The average value returns obtained is 0.111497% stock which means during the period of the investment, investors get a profit of 0.111497%% per day of the total funds invested in the shares of PT Bank Rakyat Indonesia (Persero) Tbk.

Comparison of Holt and Brown's Double Exponential Smoothing Methods in The Forecast of Moving Price for Mutual Funds

Mutual funds are one of the promising investment media where the risk is directly proportional to the size of investment growth. With proper forecasting of NAV price movements will greatly help investors to make purchases and sales transactions, therefore the authors offer the use of two different forecasting methods namely Brown's method and Holt method in double exponential smoothing to get predictions of NAV price movements. The effectiveness of the use of the method will be measured from the value of Mean Average Percentage Error (MAPE). From the calculation results obtained by the data that the Holt method produces forecasting for 1809,657 with the best α value of 0.6 and MAPE of 0.644373568, while for the Holt method obtained forecasting value of 1810,924 with the α value and the best β value of 0.9 and 0.1 and the smaller MAPE value of 0.61604262 . Looking at the amount of MAPE generated, the Holt method has a smaller forecasting error rate when compared to Brown’s method.

Power system dynamic state estimation using prediction based evolutionary technique

In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance.

Determinação das Técnicas de Previsão de Demanda para a Manufatura de Ônibus do Brasil

O presente artigo objetiva determinar os modelos de previsão de demanda que se adequam com maior precisão a manufatura de ônibus do Brasil. A fim de facilitar a visualização, separou-se o conjunto conforme os quatro principais tipos de carrocerias produzidas nacionalmente, dos quais foram submetidos a cada uma das sete técnicas de previsão, sendo escolhidos, segundo as menores estimativas de erro, o Amortecimento Exponencial Simples (Urbano), o Amortecimento Exponencial Simples com Taxa de Resposta Adaptativa (Rodoviário e Intermunicipal) e a Média Móvel (Micro-Ônibus), que permeia os períodos compreendidos entre Janeiro de 2010 a Março de 2013.Palavras-Chaves: Previsão de demanda, Manufatura de ônibus, Competitividade, Gestão estratégica.Determination of Demand Forecasting Techniques for the Buses Manufacture in BrazilAbstract: Faced with a scenario where the current global strategic management has a crucial role in the conduct of organizations, the demand for forecasting-related techniques are essential to check at the moment the manner in which the company will behave in the future. To this, this article aims to determine the demand forecasting models that adjusts with better precision for the buses manufacture in Brazil. The justificative of this approach is related to the necessity of applications that aid in the establishment of goals and prognostics more consistent to the reality if compared to the traditional methods of verification, seen its intrinsic complexity, considered highly customized, beyond helping in the moment of formation the inventory of feedstock and inputs to the future periods. Therefore, it can be affirmed that the arrangement of the subjects proposed in this research regarding the thematic can be treated as an inedit issue in analogy to the conjuncture addressed. In order to facilitate the visualization of market behavior, the data was splitted in a set of four main types of national products manufactured (Urban, Road, Intermunicipal and Micro buses). These were submitted to the seven forecasting techniques approached in this study, Simple Moving Average, Double Moving Average, Weighted Moving Average, Simple Exponential Smoothing, Adaptive Response Rate Simple Exponential Smoothing, Brown's Double Exponential Smoothing Method, and the Holt's Method, chosen according their ability of modeling the historical series. The selection of the one that better adjusts the reality found was conceived in accordance with the lowest estimative of error found for each measures proposed. By consequence, the Simple Exponenteial Smoothing (Urban), Adaptive Response Rate Simple Exponential Smoothing (Road and Intermunicipal) and the Moving Average (Micro-bus), submitted to a database that permeates the months comprised between January 2010 and March 2013, were indicated as the most propense to generate better estimatives to the context approached. In relation to the objective, it is possible to affirm that the present article has accomplished the proposed, where the models of demand forecast chosen in each segment have adjusted in a reliable way to the reality, what turns possible in the future the achievement of a more accurate projections regarding to the context of sales in each one of the cases. As future expectations, it is waited the application of the models selected in the next demand forecastings for the national bus productions, in order to detail the presented research to each one of the companies addressed, beyond the continuous seek por other techniques able to deliver a better accuracy in the results.Keywords: Forecasting, Bus manufacturing, Competitiveness, Strategic management.

Forecasting Tourism: Expenditures versus Arrivals

This article examines issues relating to the measurement and forecasting of international tourist expenditures and tourism arrivals. It shows that the two series fluctuate differently, and examines the accuracy of six different forecasting techniques (time series and econometric causal models) to forecast tourism expenditures. The results show that the accuracy of the forecasts differs depending on the country being forecast, but that the no-change model and Brown's double exponential smoothing are, overall, the two most accurate methods for forecasting international tourism expenditures.

A computer program for time series forecasting using single and double exponential smoothing techniques

This paper presents a microcomputer program for time series forecasting. The program has been developed in GW-BASIC for Zenith 150 microcomputers which are IBM PC compatible. It utilizes Single exponential smoothing, Adaptive-response-rate single exponential smoothing, and Brown's double exponential smoothing methods to forecast the future values of a given time series. The program produces plots of the original time series and forecasted series as well as forecasting errors. It computes 90% and 95% confidence intervals for forecasted values and calculates the following statistics: Mean squared error, Mean absolute percentage error, Mean absolute error, Durbin-Watson statistic, and Theil's U statistic.