Double Exponential Research Articles

Identification COVID-19 Cases in Indonesia with The Double Exponential Smoothing Method

The time-series approach is a method used to analyze a series of data in a time sequence to estimate the value of a series in the future. This article will identification the COVID-19 case model in Indonesia using the Double Exponential Smoothing Method. The Double Exponential Smoothing method is one method that can be used to optimize the estimation of the ARIMA model with smoothing parameters α. The data used is sourced from the National Disaster Management Agency which was released starting March 2, 2020. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the Covid-19 case in Indonesia following the ARIMA model (0,1,1).

PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value. The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.

Grey Double Exponential Smoothing Dengan Optimasi Levenberg-Marquardt Untuk Peramalan Volume Penumpang Di Bandara Soekarno-Hatta

Aircraft has became the best choice for long distance traveling because it has shortest travel time than any other transportations. Moreover, in recent years, aviation industries have competed for providing low cost flight so that it can also be enjoyed by middle class society. Thus escalate the popularity of aircraft as economical carrier. Knowing the volume of passengers in advance will help government and related institutions to effectively providing facilities. The volume of passengers can be predicted using classic model such as double exponential smoothing model which is simpler and has high accuracy. However, the randomness of Indonesian passenger volume data cause double exponential smoothing (DES) cannot follow both data pattern and data trend. Moreover, classic model often encounters overfitting where the prediction is bigger than the actual data. Therefore, we employed Grey Method applied on DES (GDES) to overcome this problem. GDES enabled the researcher to perform better data fitting because it would generate smoothing curve which showed clearer trend. As the result, although GDES fitting curve had higher error measurement (MSE) than DES, the forecasting result of GDES was more precise than DES. Keyword: Double Exponential Smoothing, Grey Method, Levenberg-Marquardt

Option Pricing under Double Stochastic Volatility Model with Stochastic Interest Rates and Double Exponential Jumps with Stochastic Intensity

We present option pricing under the double stochastic volatility model with stochastic interest rates and double exponential jumps with stochastic intensity in this article. We make two contributions based on the existing literature. First, we add double stochastic volatility to the option pricing model combining stochastic interest rates and jumps with stochastic intensity, and we are the first to fill this gap. Second, the stochastic interest rate process is presented in the Hull–White model. Some authors have concentrated on hybrid models based on various asset classes in recent years. Therefore, we build a multifactor model with the term structure of stochastic interest rates. We also approximated the pricing formula for European call options by applying the COS method and fast Fourier transform (FFT). Numerical results display that FFT and the COS method are much faster than the numerical integration approach used for obtaining the semi-closed form prices. The COS method shows higher accuracy, efficiency, and stability than FFT. Therefore, we use the COS method to investigate the impact of the parameters in the stochastic jump intensity process and the existence of the process on the call option prices. We also use it to examine the impact of the parameters in the interest rate process on the call option prices.

Simulation of meteorological drought using exponential smoothing models: a study on Bankura District, West Bengal, India

Water scarcity and drought management is the burning issue in India and hence needs serious attention of researchers to develop rigorous plan and management. Areas that belong to various plateaus, e.g., Chotanagpur plateau, Deccan plateau, etc., are mostly affected by drought in India. In the past decade, Bankura District of West Bengal, which belongs to northeast part of Chotanagpur plateau, faced severe drought several times. However, the assessment of drought scenario in this area is far from conclusive statement till date. In this paper, we simulate standardized precipitation index (SPI) using double exponential (DE) and Holt–Winter exponential smoothing model (HW) for several time steps (e.g., 3 months, 6 months, 12 months, 24 months and 48 months) in the time period of 1979–2014. The comparative analysis between two models indicates that DE is more accurate one. DE is observed with relatively low root mean squared error (RMSE) and high R2 value. Furthermore, drought-prone zones are demarcated using combined scores of principal component analysis (PCA) and those combined scores are estimated using actual, HW and DE simulated SPI in several time steps. At the shorter (3 and 6 months) and longer time step (12, 24 and 48 months), the PCA demonstrates almost same results. The western and northwestern blocks of the district are severely affected by drought, and the southern portions are at mild condition. Spatially distributed RMSE in every time steps is also high in northwestern portions of the study region. Our result may be useful to understand the pattern of drought to take necessary action in management of water resources in Bankura District, West Bengal. Moreover, the study uses an unique methodology to simulate and assess meteorological drought, which is applicable in any region of the world.

Comparison of Double Exponential Smoothing for Holt’s Method and Artificial Neural Network in Forecasting the Malaysian Banking Stock Markets

Forecasting stock market has been the centre of attraction among investors for a long period of time. Investors are always forecasting their return on investment in the stock market before they start to invest. In this study, to forecast on the stock market price, the monthly closing stock prices data from the Malaysian stock markets, namely AM001 Berhad, CI002 Berhad, HL003 Berhad and PB004 Berhad from 2008 to 2017, are examined for predictability results using Double Exponential Smoothing (DES) for Holt’s method and Artificial Neural Network (ANN). The data is partitioned into two parts due to different purposes.A sample data consisting of 96 months data from 2008 to 2015 was used for the estimation parameter and modeling part. Meanwhile, the evaluation part to validate the DES for Holt’s method and ANN was conducted using out-of-sample data involving 24 months data from 2016 to 2017. Three error measurements, MAD, MSE and RMSE, have been used in the evaluation to compare the performance of these two forecasting methods. The statistical analysis results show that Holt’s method is superior to ANN model and when using real values, it could accurately predict future price movements in the Malaysian stock markets. The outcomes from this study suggest that it is worthwhile to investigate the predictability and profitability of forecasting models. Keywords: Artificial Neural Network, Double Exponential Smoothing, Stock Market, Time Series Forecasting Models

Managing Internal Bullwhip Effect to Plan Product Distribution in a Garment Factory

XYZ is a family garment business established in 2004. XYZ produces and sells garments for 6-12 years old boys with brands like Forboys, Wellboy, Nashkid, Yepao, and Kid Dreams. The types of garments are oblong, setelan and wangky. XYZ has three garment Cut-make-trim (CMT) partners in Rangkas, Serang and Pandeglang. The three CMT partners has been giving XYZ neat and tidy garment products, ready to compete in Tanah Abang wholesale center. Based on the demand forecast calculation XYZ sends its order for production to the CMTs. In return, based on the quantity ordered, the CMTs order the raw materials from XYZ. This study attempts to measure the “internal” bullwhip effect which is observed as the fluctuations of orders sent by the CMTs to XYZ, based on the production orders from XYZ to the CMTs. XYZ experienced high inventories and its mounting costs due to this internal bullwhip effect. Further, the method used in this study investigate the appropriate forecasting method. The best forecasting method is the Double Exponential Smoothing, which also results in the reduction in the bullwhip effect ratio, and improvement of scrap factor (around 10.60%). The reduction of BE ratio is quite significant from 0.344, 0.242 and 0.717 for oblong, setelan and wangky product types respectively.

The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera

In Mathematics, Forecasting is scientific. Forecasting uses statistical techniques to describe the future using previous data. Based on North Sumatra BPS data, the number of foreign tourists coming to North Sumatra fluctuates. Therefore, forecasting the number of foreign tourism needs to be done so that tourism actors can prepare themselves. The purpose of this study is to compare time series forecasting methods, namely Double Moving Average (DMA) and Double Exponential Smoothing (DES). Then the number of tourists is predicted using the best method, the method with the smallest MAPE. The research data in the form of secondary data obtained from BPS as many as 108 data, from January 2010 to December 2018. The calculation process uses Ms. Excel. The results showed that the 12th time order on DMA had the smallest MAPE value, which was 14.12%. Whereas the DES Brown method with and DES Holt yielding MAPE values respectively 12.71% and 12.21%. Forecasting the number of foreign tourists coming to North Sumatra in 2019 using the DES Holt method.

Sustainable industrial systems through strategic laboratory equipment industry

This paper conveys the theoretical perspectives of sustainable industrial systems through Strategic Laboratory Equipment Industry, within scope of PT. Promedika Sejahtera (PPS). PT Promedika Sejahtera is a company engaged in the laboratory equipment industry, that need its strategic touch on its Strategic Laboratory Equipment. Products sold by the company are Oxygen, Medical Equipment, Ultraviolet System, Activated Carbon Filter, Pempers, and Underpad. Products that exist in the company are not in their own production in the production from abroad. PT Promedika Sejahtera merely sell the laboratory equipment. Yet, the company has various types of consumers namely hospitals, hospital cooperatives, pharmacies, factories, and water equipment RO shops. In this paper, strategic laboratory equipment industry refers specifically to inventory within the supply chain management perspectives. This paper constitutes collaboration with other papers on Economic Order Quantity (EOQ), Re-Order Point (ROP), and Safety Stock (SS), that convolutes Forecasting, ABC Analysis, and Partial Least Square (PLS). Yet, this paper refers to its focus merely on Forecasting, Demand Pattern, Exponential Smoothing and EOQ. As part of conclusion, this paper conveys several highlighted remarks. Those remarks refer to ABC Analysis. The grouping of goods using ABC Analysis Method is divided into three groups: A with 80% priority, group B with 20% priority and group C with 10% priority. The number of goods in group A is 12 items, group B 18 item type and group C are 30 items of goods. Forecasting demand required by the Company for 2018 based on priority group A can be done using the Double Exponential Smoothing (Holt’s) Method because the pattern pattern possessed from the previous year’s sales data has no seasonal but has a random trend. The results of these calculations can be seen from the table of existing research results. Ultimately, Future research is deemed indispensable within novelty purpose for the industrial system sustainability within Strategic Laboratory Equipment Industry.

On order statistics from Laplace-type distributions

We obtain the probability density function (pdf) of a single order statistic (OS) and the joint pdf of two OS based on a random sample Zii=1n from Z=UX−(1−U)Y, where U,X and Y are independent with U∼Bernoulliβ. We drive formulas for calculating the moments and the product moments of OS based on Zii=1n. We apply the obtained results to the important class of Double distributions. This class includes the Double Exponential, the Double Logistic and the Double Pareto distributions.

Forecasting Fisheries Production in Indonesia

The abundance of water resources avails Indonesia an excellent advantage in terms of the development of the capture and aquaculture fisheries. In recent years, Indonesian fish production has shown an increasing trend. The capture and aquaculture fisheries reached 6.6 million and 16.0 million tonnes respectively in 2016. The growing trend was translated into an average contribution of 2.4 percent towards the national GDP in 2013-2017. However, the absence of forecasting methods and data on fisheries production's potential growth contributes to ineffective policy interventions that require optimum production. Therefore, this study's main objective is to find the most accurate forecasting method for Indonesia's fisheries production. This research utilized the quarterly data of Indonesian fisheries production in 2000-2018 obtained from the Ministry of Marine Affairs and Fisheries. A comparative analysis of the Double Exponential and Seasonal ARIMA (SARIMA) method was undertaken to arrive at the most accurate forecasting method. The study findings broadly revealed that Indonesia's fish production was on an increasing trend, with aquaculture fisheries' contribution outweighing the capture fisheries in recent years. Furthermore, the SARIMA method was found to be the most accurate forecasting method compared to the Double Exponential method. The findings are useful for the government and related stakeholders for enhancing fish productivity in Indonesia. In addition, SARIMA methods could be used to forecast the fish production in upcoming years for better policy, strategy, and decision-making in developing the fisheries sector in Indonesia.

Computing Four-Center Two-Electron Coulomb Integrals Using Exponential Transformations and Trapezoidal Rule

The numerical evaluations of the four-center two-electron Coulomb integrals are among the most time-consuming computations involved in molecular electronic structure calculations. In the present paper we extend the double exponential (DE) transform method, previously developed for the numerical evaluation of threecenter one-electron molecular integrals [J. Lovrod, H. Safouhi, Molecular Physics (2019) DOI:10.1030/0026867.2019.1619854], to four-center two-electron integrals. The fast convergence properties analyzed in the numerical section illustrate the advantages of the new approach.

A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods

Paper aims Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods. Originality With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry. Research method The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it. Main findings The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient. Implications for theory and practice The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.

Research of Forecasting on Tourist Arrivals to Malaysia

Tourists get attracted towards Malaysia because of our culture and geography. Apart from heritage and culture, the tourists from all over the world visit here for various purpose. Therefore, forecasting tourist arrivals with high level of accuracy becomes important because it can ensure the development of tourism industries. So, this study focuses on tourist arrivals to Malaysia. This paper attempts to define the component of patterns exist in the time series data, to determine the most suitable model best fits in data series by using the error measure that are Mean Square Error (MSE) and Mean Absolute Deviation (MAD) and to forecast the one-step ahead forecast on the best model. In this study, data of tourist arrivals to Malaysia has been obtained from January 2000 until December 2018. All 228 monthly data were analyzed by using selected Univariate Modeling. The result found that tourist arrivals to Malaysia has a linear trend model and Double Exponential Smoothing with α = 0.17 was the best model for this time series.

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.

Perbandingan Metode Peramalan Jumlah Produksi Palm Kernel Oil (PKO) Menggunakan Metode Double Moving Average, Double Exponential Smothing dan Box Jenkins

One of Indonesia's significant results is oil palm. The reality of this plantation is not only owned by the government (BUMN) but also the private sector. Every period, the company does forecasting in terms of production, especially for the next period. Among them is to set production targets, company operations, and financial planning. Based on this, a study was conducted with the aim to predict the amount of palm kernel oil (PKO) production at PT. Mitra Mendawai Sejati for the next six (6) months. The method used is Double Moving Average, Double Exponential Smoothing and Box Jenkins. While the data used is historical data from the amount of palm kernel oil production for five (5) years obtained from the company. Based on the results of the study, received the forecast value of the Suayap output in 2019 with the best method, namely the Double Exponential Smoothing method. Based on the forecast we got in January at 949181.5 Kg, February at 963505.8 Kg, March at 977830.1 Kg, April at 992154.4 Kg, May at 1006478.6 Kg and June 1020802.9 Kg with MSE value of 47031163817, and RMSE of 216866.7 and parameter values (optimum weighting) for α = 0.616667 and β = 0.1548939

DOUBLE EXPONENTIAL EULER–SINC COLLOCATION METHOD FOR A TIME–FRACTIONAL CONVECTION–DIFFUSION EQUATION

In this research, a new version of Sinc-collocation method incorporated with a Double Exponential (DE) transformation is implemented for a class of convectiondiffusion equations that involve time fractional derivative in the Caputo sense. Our approach uses the DE Sinc functions in space and the Euler polynomials in time, respectively. The problem is reduced to the solution of a system of linear algebraic equations. A comparison between the proposed approximated solution and numerical/exact/available solution reveals the reliability and significant advantages of our newly proposed method.

Valid implementation of Sinc-collocation method to solve the fuzzy Fredholm integral equation

The main purpose of this work is to validate the numerical solution of fuzzy Fredholm integral equation (IE) by applying the Sinc-collocation method (S-CM) based on the double exponential (DE) and single exponential (SE) decays. This approach is based on the discrete stochastic arithmetic (DSA) which is able to validate the results with optimal solution and rely the proposed algorithm in comparison with the floating-point arithmetic (FPA). In order to achieve this goal, the fuzzy CESTAC11Controle et Estimation Stochastique des Arrondis de Calculs. method and the CADNA22Control of Accuracy and Debugging for Numerical Applications. library are applied. The accuracy of the S-CM in both SE and DE cases is discussed by proving the theorems. Also, two numerical algorithms based on the S-CM in SE and DE decays by applying the fuzzy CESTAC method and CADNA library are presented. The termination criterion in each algorithm is based on the Hausdorff distance to be an informatical zero. By solving the examples, not only the optimal approximation and the optimal iteration of the S-CM can be found but also similar to the non-fuzzy case, it is shown that the DE precision is more accurate with faster convergence rate than the SE.

Poverty Line Forecasting Model Using Double Exponential Smoothing Holt’s Method

The research aims to forecast the Poverty Line, to help a government obtain accurate and fast information. The method used in this research is Double Exponential Smoothing Holt’s Method. This method is a part of the data based on time series analysis. The research applies the forecasting theory to produce a poverty line forecast for the coming year. Next, this research is analyzing data patterns and determine the best parameter values. Double Exponential Smoothing Holt’s method uses the parameters Alpha (α) and Gamma (γ). To determine the best parameter value is to use the trial and error method. The best parameter value produces the smallest value of MAPE (Mean Absolute Percentage Error). The data pattern shows the trend, meaning that the Double Exponential Smoothing Holt’s method is appropriate to be used in this research. The parameter values generated from the trial and error methods are Alpha (α) of 0.7 and Gamma (γ) of 0.1, which produced the smallest measure of accuracy, in this research using MAPE. By observing the results of the forecasting that has been done, this forecasting model has a very good performance. Poverty Line value will keep increasing, in accordance with increasing consumption patterns and rising prices of basic necessities.