Double Exponential Research Articles

Ceria and nitrogen decorated Ca-BTC-derived carbon with residual CaO for CO2 adsorption

Calcium oxide (CaO) is a high-temperature CO2 capture sorbent. However, it suffers from CaO aggregation for CO2 scrubbing after looping cycles. To disperse and secure CaO, in this research, we pyrolyzed Ca-based metal-organic frameworks (MOFs) Ca-BTC for CaO on MOF-derived carbon (MDC). Furthermore, ceria or nitrogen was introduced into Ca-MDC by Ce2+ or DABA ligand addition to Ca-BTC. Compared to pure CaO, Ca-MDC maintained 30 % better CO2 adsorption stability after 10 cycles. In comparison, the CO2 uptake of CeO2-decorated Ca-MDC kept at about 100 % after 10 cycles, which could be attributed to oxygen vacancy in CeO2. The highest CO2 adsorption reached 17.10 mmol/g in N-doped Ca-MDC with 80 % stability. All CaO on MDC samples had CO2 adsorption exceeding the theoretical stoichiometric ratio 1:1 of CaO:CO2, implying plausible CO2 spillover from CaO to carbon matrix. For kinetics analyses, the pseudo-second order (PSO) model showed a better fit than the linear driving force (LDF) model, suggesting strong chemisorption in CaO as expected. The double exponential (DE) model was applied to differentiate CO2 chemisorption and surface diffusion and showed much higher diffusion rate constants in Ca-MDC than CaO, suggesting a possible CO2 spillover phenomenon.

Computing the matrix exponential with the double exponential formula

Abstract This article considers the computation of the matrix exponential e A {{\rm{e}}}^{A} with numerical quadrature. Although several quadrature-based algorithms have been proposed, they focus on (near) Hermitian matrices. In order to deal with non-Hermitian matrices, we use another integral representation including an oscillatory term and consider applying the double exponential (DE) formula specialized to Fourier integrals. The DE formula transforms the given integral into another integral whose interval is infinite, and therefore, it is necessary to truncate the infinite interval. In this article, to utilize the DE formula, we analyze the truncation error and propose two algorithms. The first one approximates e A {{\rm{e}}}^{A} with the fixed mesh size, which is a parameter in the DE formula affecting the accuracy. The second one computes e A {{\rm{e}}}^{A} based on the first one with automatic selection of the mesh size depending on the given error tolerance.

Exponential series approximation of the SIR epidemiological model

IntroductionThe SIR (Susceptible-Infected-Recovered) model is one of the simplest and most widely used frameworks for understanding epidemic outbreaks.MethodsA second-order dynamical system for the R variable is formulated using an infinite exponential series expansion, and a recursion relation is established between the series coefficients. A numerical approximation scheme for the R variable is also developed.ResultsThe proposed numerical method is compared to a double exponential (DE) nonlinear approximate analytic solution, which reveals two coupled timescales: a relaxation timescale, determined by the ratio of the model’s time constants, and an excitation timescale, dictated by the population size. The DE solution is applied to estimate model parameters for a well-known epidemiological dataset—the boarding school flu outbreak.DiscussionFrom a theoretical standpoint, the primary contribution of this work is the derivation of an infinite exponential, Dirichlet, series for the model variables. Truncating the series yields a finite approximation, known as a Prony series, which can be interpreted as a sequence of coupled exponential relaxation processes, each with a distinct timescale. This apparent complexity can be approximated well by the DE solution, which appears to be of main practical interest.

The Use of Brown's Double Exponential Smoothing Method to Predict Harvest Yields in Horticultural Crops

Agriculture stands as a pivotal sub-sector within the economy of North Aceh. Among its primary commodities are horticultural crops, encompassing the cultivation of vegetables, fruits, medicinal plants, and ornamental flora. In endeavors to boost agricultural productivity and efficiency, the utilization of harvest prediction methodologies has grown increasingly indispensable. This study relies on historical harvest data spanning from 2017 to 2022 to forecast crops such as leafy greens, fruits, and medicinal plants. The selected plants for prediction include spinach, water spinach, cucumber, banana, durian, rambutan, ginger, lesser galangal, and turmeric. Data analysis employs Brown's double exponential smoothing method, selecting the α (alpha) parameter that minimizes the Mean Absolute Percentage Error (MAPE) for accurate forecasting. Spinach is anticipated to yield 1239.9508 quintals, with an α (alpha) parameter of 0.9 and a MAPE of 38.46%. Water spinach is forecasted to yield 2069.75 quintals, with an α (alpha) parameter of 0.5 and a MAPE of 18.14%. Cucumber is projected to yield 1023.22432 quintals, with an α (alpha) parameter of 0.4 and a MAPE of 31.51%. Consequently, the highest projected yield is for water spinach at 2069,75 quintals.

Double Moving Average and Double Exponential Smoothing Method in Sales Forecasting

At present, predictions concerning sales are predominantly based on historical sales data, a practice that frequently yields inaccurate results. Such inaccuracies can lead to substantial financial losses, compelling organizations to lower the capital expenditures associated with certain products to offset these losses. This issue primarily arises from the failure to employ an appropriate forecasting methodology, resulting in estimations that lack reliable analytical foundations. This research aims to evaluate the efficacy of the Double Moving Average method compared to the Double Exponential Smoothing technique for sales forecasting, specifically through a case study involving herbal products. This study also seeks to analyze and compute the Mean Absolute Percentage Error (MAPE) for each forecasting method based on prior observations and research. The analysis draws on a dataset comprising 200 sales transactions from the five top-selling products collected between April 2022 and April 2024. The outcomes of this investigation provide MAPE values derived from the sales data, followed by a comprehensive summation of the calculated MAPE for each method. For June 2024, the results recorded are slightly higher in some cases. For example, the DES result for HNI Eucalyptus Oil was 42.65 with a MAPE of 0.46, while the DMA was 44.44 with a MAPE of 0.44.

Forecasting models analysis for predictive maintenance

IntroductionThis study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency.MethodsThe research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes.ResultsA comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate.DiscussionThese findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.

The Comparison of Performance Double Exponential Smoothing and Double Moving Average Methods in Seasonal Meat Stock Demand

The demand for beef supplies depended on seasonal patterns because it depends on feed supplies, especially in rural areas that still rely on natural feed. Beef supplies were part of government regulations because they were highly demanded commodities. They are livestock products that contain nutritional value to meet the protein needs of the community. Beef stocks were influenced by factors such as beef production, beef consumption, and people's income levels. In anticipating the increasing demand for beef, it is necessary to carry out forecasting to estimate the demand for meat in the future. In forecasting, various methods were used to choose the method with the lowest error rate. This research will compare the Double Exponential Smoothing (DES) with the Double Moving Average (DMA) based on the Mean Absolute Percentage Error (MAPE) measurement, one method for finding the minor error value. Based on the test results with beef supplies in Madura and conducting analysis, it can be concluded that the method with the smallest MAPE value is the Double Exponential Smoothing method, with the smallest MAPE value of 9.50% at an alpha parameter of 0.5. In testing with the Double Moving Average method, by determining the best MAPE value, the best time order in the Double Moving Average method is at time order parameter 2 with a MAPE value of 29.8408%. After finding the parameter with the smallest MAPE value, that parameter is used for data testing. In the measurement, data testing for data of 1 year, two years, three years, and four years. Each method has a level of error value that increases the same; a lot of data entered can affect the size of the MAPE value. So, the more data entered, the smaller the resulting error value.

Peramalan Cadangan Devisa Menggunakan Metode Double Smoothing Exponential dan Metode Fuzzy Time Series

This study aims to forecast the amount of foreign exchange reserves using two methods, namely Double Smoothing Exponential and Fuzzy Time Series. This foreign exchange reserve data is time series data, namely monthly data starting from January 2017 to February 2024 with a total of 86 monthly data. Furthermore, to see the level of accuracy of the forecast results, the Mean Absolute Percentage Error (MAPE) and MSE values ​​are used where the smaller the error value produced, the more accurate the forecast results from the Double Smoothing Exponential Holt and Fuzzy Time Series Cheng methods. The results of forecasting the amount of foreign exchange reserves in March 2024 using the Holt method are 144434.19 and for the Cheng method are 140008.032. The double exponential smoothing method has an MSE value of 8625715.609 and a MAPE value of 1.69%. Meanwhile, with the fuzzy time series method, the MSE value is 11651319.400 and the MAPE value is 1.96%. Forecasting using these two methods has a very good level of accuracy, because it has a MAPE value below 10%. Based on the results of this analysis, it can be seen that the double exponential smoothing method from Holt has a smaller forecast error size than the Cheng fuzzy time series method. Thus, it can be concluded that the Double Exponential Smoothing method is a better method for predicting the amount of foreign exchange reserves.

PERAMALAN NILAI TUKAR PETANI PROVINSI ACEH: DITINJAU DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN HOLT WINTER

This research aimed to compare the performance of the Double Exponential Smoothing and Holt-Winter methods in forecasting the Farmer Exchange Rate (NTP) for the Horticulture Farmer Subsector in Aceh Province. Secondary data on the NTP for the Aceh Horticulture Subsector from 2020-2023 was obtained from the Aceh Provincial Central Statistics Agency (BPS) website. The data processing was carried out using the R software. The results showed that the Double Exponential Smoothing method had a Root Mean Squared Error (RMSE) of 3.413201, a Mean Absolute Percentage Error (MAPE) of 2.85835, and a Mean Percentage Error (MPE) of -0.5980113. Meanwhile, the Holt-Winter's Additive method had an RMSE of 4.055652, a MAPE of 3.055992, and an MPE of 0.2534276. The best forecasting method was selected based on the smallest RMSE, MAPE, and MPE values, which was Double Exponential Smoothing. The 12-month forecast using the Double Exponential Smoothing method indicated that the lowest NTP would occur in January 2024 at 106.7195, while the highest NTP would be in December 2024 at 113.6443

PENERAPAN DOUBLE EXPONENTIAL SMOOTHING UNTUK MEMPREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU

This research aims to find out or predict the number of new students who will register for admission at the UNTRIB Kalabahi campus in 2024. The data collection technique used in this research is secondary data obtained from BAAK (Student Academic Administration Bureau) UNTRIB Kalabahi from 2007 until 2023 there are 17 data. The method used in this research is the Double Exponential Smoothing method from Brown to analyze the number of new students in 2024 based on new student data in 2007-2023 by testing using 9 alpha value parameters, namely 0.1 to 0.9 by looking at the Mean value The smallest Absolute Percentage Error (MAPE). The research results show that the smallest MAPE value is 6.35% with an alpha parameter value of 0.5. The estimated number of new student admissions in the 2024/2025 academic year is 590 people

Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.

Forecast Analysis of Fruit Supply Using Time-Series Method: A Study at PT Aerofood Indonesia

PT Aerofood Indonesia is an entity operating in the aviation logistics industry, specifically as a partner of the Garuda Indonesia airline. The main problem faced is the tendency to have excessive supplies of raw materials, especially in the aspect of purchasing fruit because it is susceptible to damage. This phenomenon often recurs and peaks beyond dry goods capacity limits, especially the availability of papaya fruit which often exceeds capacity by more than 50%. Therefore, this research aims to identify the optimal forecasting method for papaya fruit to overcome the problem of excessive stock. In this research, four forecasting approaches were tested, namely Trend Analysis, Single Average Exponential, Double Average Exponential, and Holt's Winter Method. Forecasting calculations were carried out using a manual approach and supported by Minitab 18 software. The research results showed that the Holt's Winter method with a multiplicative approach produced the lowest Mean Absolute Percentage Error (MAPE), namely 16%. The Holt's Winter method with a multiplicative approach has proven to be effective in producing accurate forecasts. By implementing this recommended forecasting method, it is hoped that companies can be more efficient in managing inventory and reduce the impact of losses due to excess stock.

A Double Exponential Particle Swarm Optimization with non-uniform variates as stochastic tuning and guaranteed convergence to a global optimum with sample applications to finding optimal exact designs in biostatistics

Nature-inspired metaheuristic algorithms, like Particle Swarm Optimization (PSO), are powerful general-purpose optimization tools but they invariably do not come with rigorous theoretical justifications and can fail to find a global optimum. By treating PSO as a random search optimization process and repairing the famous Global Search Convergence Theorem by imposing an additional condition in the proof, we create a novel theory-based algorithm called the Double Exponential Particle Swarm optimization algorithm (DExPSO) that converges to a global optimum. In particular, we show the common practice of using uniform variates as stochastic components in PSO and related algorithms does not satisfy the sufficient conditions in DExPSO and hence may provide a reason why PSO and other nature-inspired algorithms, like QPSO, LcRiPSO, and CSO can fail to converge. Additionally, in more complicated design problems, we show that DExpSO tends to converge to the support points of the optimal design more frequently and faster than PSO and its variants do. Moreover, there is a possibility to modify other PSO variants to DExPSO variants, and such hybridization offers promising improvement in the quality of the global search. Our applications include finding designs that minimize the integrated mean squared prediction error and locally D-optimal exact designs for a 68-compartmental model to assess radioactive particles retained in the human lung after exposure. Because PSO, and more generally, metaheuristics are used across disciplines, including ecology, pharmacokinetics and pharmacodynamics studies, agriculture, engineering, and computer science, there are potentially broad and deep implications of our results.

Comparison of Double and Triple Exponential Smoothing Methods for Rainfall Prediction

Comparison of Double and Triple Exponential Smoothing Methods for Rainfall Prediction

Indonesia's Breakthrough in Optimized Yarn Forecasting for Textile Demand Accuracy

PT. XY, a textile company specializing in woven sarongs, faces fluctuating demand during Islamic religious celebrations, impacting production. In Ramadhan 2023, production increased by 30%, but warp yarn availability was insufficient. This study forecasts warp yarn production over twelve periods, comparing Double Exponential Smoothing Holt’s (DES) and Holt-Winter’s Exponential Smoothing (WES) methods, optimized using the golden section method. Using historical data from January 2021 to April 2023, WES with golden section parameters (α1 = 0.67387, β1 = 0.08756, γ2 = 0.85408) achieved the best accuracy with a MAPE of 5.5437%. The WES method is recommended for improving production planning at PT. XY, with future research suggested to explore production correlations and procurement costs. Highlight: Demand Fluctuation: PT. XY experiences significant demand changes during Islamic religious celebrations. Forecasting Methods: Comparing DES and WES methods for predicting warp yarn production. Optimal Accuracy: WES with golden section optimization achieved the lowest MAPE of 5.5437%. Keywoard: Textile Industry, Warp yarn forecasting, Production Planning, Holt-Winter's method, Golden section optimization

Neural Network Sales Revolution Outperforms Exponential Smoothing

This research addresses the challenges faced by food manufacturing companies, focusing on UD. XYZ as a case study. With fluctuating sales levels causing raw material buildup and shortages, the study proposes an improved sales forecasting method to enhance raw material control. By comparing Artificial Neural Network (ANN) and Double Exponential Smoothing Holts, the research aims to optimize inventory management and production processes. Results indicate ANN's superiority over Holts, with an accuracy rate of 0.118 compared to 11.639. The ANN model accurately forecasts sales for the upcoming twelve-month period, highlighting a decline from July 2023 to May 2024. Implementing advanced forecasting methods can mitigate raw material-related risks and enhance operational efficiency for companies like UD. XYZ. Highlight: Enhanced sales prediction methods crucial for inventory planning. Artificial Neural Network outperforms traditional forecasting techniques. Improved forecasting mitigates raw material shortages and excesses. Keywoard: Sales forecasting, Artificial Neural Network, Raw material control, Inventory management, Production optimization.

Predicting LAN switch failures: An integrated approach with DES and machine learning techniques (RF/LR/DT/SVM)

This research paper introduces an innovative approach to predicting failures in Local Area Network (LAN) switches, combining Double Exponential Smoothing (DES) with a suite of Machine Learning (ML) algorithms including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM). The primary objective of this study is to enhance the accuracy and timeliness of LAN switch failure predictions, thereby facilitating more proactive and effective network management. Our methodology involves the integration of DES for trend analysis and forecasting in time-series data, with the advanced predictive capabilities of the aforementioned ML algorithms. This hybrid approach not only leverages the strengths of DES in identifying underlying patterns in failure data but also capitalizes on the diverse predictive models to handle various aspects of failure prediction more robustly. The paper details the process of data collection, preprocessing, and the specific application of DES and each ML algorithm to the dataset. A notable contribution of this research is the development of a framework that effectively combines the output of DES with ML models, leading to a significant improvement in predictive accuracy as compared to traditional methods. Through rigorous testing and validation; the proposed approach demonstrated a marked increase in the precision and reliability of failure predictions. The results indicate that the integration of DES with ML algorithms can substantially aid in preemptive maintenance and decision-making processes in LAN management. The implications of these findings are profound, suggesting that such a combined approach can greatly enhance network stability and efficiency. While the focus of this study is on LAN switches, the methodology has the potential for broader applications in various fields of network management and predictive maintenance.

Prediksi Jumlah Calon Siswa Baru Menggunakan Metode Double Exponential Smoothing Brown (Studi Kasus: SMK Ma’arif NU 1 Purwokerto Tahun Pelajaran 2024/2025)

The number of new students registering at SMK Ma'arif NU 1 Purwokerto from 2010 to 2023 has increased and decreased. This fluctuating pattern is a problem faced in determining strategies to attract new students to register. Large fluctuations in student numbers can cause instability in school revenues, which affects all operational aspects and will impact the school's reputation in the eyes of the community and prospective students. In this regard, it is necessary to take action, namely forecasting the number of new students registering for the 2024/2025 academic year. The aim of this research is to determine the prediction of the number of prospective new students at SMK Ma'arif NU 1 Purwokerto using theDouble Exponential Smoothing Brown in the 2024/2025 academic year. From the research results, it was obtained that the number of new students in the 2024/2025 academic year was based on overall data on students who registered with the best α parameter α = 0.2 with a value ofMAD = 6,02, MSE = 68,37, andMAP= 20.23%, namely 42 students. The forecast for the number of new students registering at SMK Ma'arif NU 1 Purwokerto based on overall data from the school has increased from the previous year. Value resultsMAPshows that the forecasting that has been carried out falls into the category of adequate forecasting method capabilities so that it can be used to predict the number of new students enrolling at SMK Ma'arif NU 1 Purwoketo and the predicted value for the following year can be known.Keywords: predictions, forecasting, new students, Double Exponential Smoothing Brown

PREDIKSI JUMLAH PENGUNJUNG PERPUSTAKAAN MENGGUNAKAN DOUBLE EXPONENTIAL SMOOTHING

The library is an information center that needs to be managed effectively. The large number of library visitors reflects the efforts made to increase student literacy. This research aims to predict the number of visitors to the Faculty of Science and Technology Library, Jambi University using the Double Exponential Smoothing Holt method which is predicted by giving smoothing values ​​to a series of previous observations with parameter values ​​α=0.775 and β= 0.367. The data used in this research is secondary data obtained from daily data from the Jambi University Faculty of Science and Technology library, totaling 125 data. Predictions show that the number of visitors in the next 5 periods is expected to increase by 48 visitors. The forecast accuracy value from data on the number of visitors to the Jambi University Faculty of Science and Technology library uses a MAPE (Mean Absolute Precentage Error) value of 18.878%, meaning the forecasting model is in the good category.

Perapan Metode Double Exponential Smoothing pada Prediksi Nilai Tukar Petani di Provinsi Sumatra Barat Tahun 2023

West Sumatra is one of the provinces in Indonesia where the agricultural sector is very important for the regional economy. To encourage the development of the agricultural sector, an appropriate strategy is needed for each location. One of the efforts to encourage the development of the agricultural sector is to pay attention to the availability of agricultural facilities and infrastructure, so that there are no obstacles for producers that increase production costs and harm farmers. The purpose of this study was to determine the model and prediction results of Farmer Exchange Rate in West Sumatra using the Double Exponential Smoothing Method from January 2023 to June 2023. The prediction equation for NTP in West Sumatra Province in 2023 obtained is 〖 F〗_(t+m)=110,5049+(m)0,1773with a value of α = 0.99, β = 0.0115 and MAPE value = 0.99%. The Exchange Rate in West Sumatra from January 2023 to June 2023 tends to increase. The increasing Farmer Exchange Rate in West Sumatra can have a positive impact on the agricultural sector and improve the economic welfare of farmers.