Double Exponential Research Articles

Honda Motorcycle Stock Forecasting System Using Double Exponential Smoothing Method (case Study of Honda Dealer PT


 Stock in the warehouse of PT. Delta Sari Agung Sidoarjo is currently unstable, therefore between in and out stock is still out of control. Forecasting is estimating the state of the future through testing the state of the past. In social life, everything is uncertain and difficult to predict accurately, so forecasting is needed. In other words forecasting aims to get forecasting that can minimize forecasting errors (forecast error) which is usually measured by mean square error, mean absolute error, and so on. The Double Exponential Smoothing method is used for forecasting by determining the amount of α (alpha), as well as the smoothing process twice and this study is compared with ANNMatlab. From the results of the comparison of the trial system forecasting and JST-Matlab there is a difference of 16 motorbikes from the remaining stock, which is for the results of forecasting 622 and JST-Matlab 638 results.

A double exponential potential for van der Waals interaction.

Van der Waals (vdw) interaction is an important force between atoms and molecules. Many potential functions have been proposed to model vdw interaction such as the Lennard-Jones (L-J) potential. To overcome certain drawbacks of existing function forms, this work proposes a double exponential (DE) potential that contains a repulsive exponential term and an attractive exponential term. This potential decays faster than the L-J potential and has a soft core. The DE potential is very flexible and its two exponential parameters can be adjusted continuously to mimic many potential functions. Combined with the isotropic periodic sum (IPS) method, the DE potential can efficiently and accurately describe non-bonded interactions and is convenient for alchemical free energy calculation.

Semicool Temperature Compensation Algorithm Based on the Double Exponential Model in the Ultrasonic Positioning System

This paper proposes a semicool temperature compensation algorithm based on the double exponential model of the transient temperature rise curve to improve the performance of the 3-D ultrasonic positioning system. The error model of the 3-D ultrasonic positioning system is derived and the importance of temperature to the positioning accuracy is demonstrated. Then, the double exponential model of the transient temperature rise curve derived by the difference principle is established through a dynamic system identification method. On this basis, the semicool temperature compensation algorithm is proposed, which includes the estimation of the initial time $t_{0}$ , the principle of the choice of parameter $k$ . The effectiveness of the algorithm is verified by temperature measurement experiments. The impact of the ambient temperature on the algorithm is also analyzed qualitatively. In the end, the semicool temperature compensation algorithm is proved to be able to improve the performance of the 3-D ultrasonic positioning system by the comparison experiments respectively under static measurement of a fixed point and dynamic measurement of a fixed line.

Forecasting Model Selection of Curly Red Chili Price at Retail Level

Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).

Forecasting Wheat Production Using Time Series Models in Pakistan

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

An Analysis of the Error Associated to Single and Double Exponential Approximations of Theoretical Poroelastic Models

The temporal evolutions of displacements, strains, stresses, and fluid pressure in a poroelastic material under sustained compression are theoretically expressed as sums of an infinite number of exponentials. However, estimation of an infinite number of time constants is impractical in experimental settings. In the past, empirical models containing a finite number of exponentials have been proposed and used to approximate the theoretical poroelastic models. At the present time, however, the degree of error associated with such approximations is unclear. In this paper, we present an analysis of the error encountered when approximating a poroelastic model containing an infinite number of exponentials with a single or double exponential model. As a testing platform, the presented error analysis is applied to the estimation of effective Poisson’s ratio (EPR) and fluid pressure in a uniform cylindrical poroelastic sample and a cylindrical poroelastic sample containing an inclusion under stress relaxation. Our results show that, when the infinite number of exponentials in the theoretical models are approximated with finite number of exponentials, significant error is invoked only in the first few time samples of the EPR and fluid pressure, while the error is negligible for the remaining time samples. We also show that, when estimating clinically relevant mechanical parameters such as Poisson’s ratio or the product of aggregate modulus and interstitial permeability, such approximation invokes small error (<3%) in comparison to the general model with infinite number of exponentials. Therefore, such approximation may be acceptable in techniques aiming at reconstructing mechanical parameters from poroelastographic data.

Forecasting mesoscale distribution of surface solar irradiation using a proposed hybrid approach combining satellite remote sensing and time series models

A new hybrid forecasting tool is developed in this study which makes use of satellite remote sensing data of surface solar irradiation coupled to a Double Exponential Smoothing time series model. The prediction capabilities of the Double Exponential Smoothing model are reported to be higher than the ARMA and NAR-Neural Network. The mean absolute percentage error of this hybrid system is revealed to be the lowest (4.89%) on average for 5 consecutive days-ahead forecasts over the years 2013–2015, with the smallest standard deviation reported throughout the year, characteristic of a highly stable and robust model (3.83 W/m2). Exploring the performance of the model for the best and worst case scenarios reveal that high prediction accuracies on both spatial and temporal scales are achievable with strong positive linear correlations of the orders of 0.928 and 0.894 respectively, averaged over the 5 days forecasts. The performance of the hybrid system is found to be higher as compared with benchmark accuracy reached by several other models employed in literature. Finally, the use of the hybrid forecasting tool developed in providing energy and grid management facilities for the island of Mauritius is also presented.

Pengendalian Persediaan Bahan Baku Nalco Water Treatment Dengan Menggunakan Metode Lot Sizing

The limited supply of Nalco raw materials from producers has become a problem for PT ABC, this has led to the control of raw material inventory at PT ABC not including good management, because in the management of raw materials the company still records inventory with manual systems and in ordering raw materials only based on estimates. From the results of the study, the forecasting method used is the Double Exponential Smoothing Holt's, Brown, and Holt Winters Additive Algorithm methods, from the three methods that are most suitable is the Double Exponential Smoothing Brown method with the smallest Mean Square Error of 256.2. Calculation of Sizing Lot by using Economic Order Quantity method, Least Unit Cost method, and Silver Meal method, of the three methods the most optimal is the Economic Order Quantity method because it has the lowest cost of Rp. 12,651,145. The calculation of Safety Stock gets 17 Pail results. and for Reorder Points for Nalco Water Treatment raw material, which is 29 Pail.

Deseasonalisation in Electricity Load Forecasting

Nowadays, there is an increasing demand for electricity however overproduction of electricity lead to wastage. Therefore, electricity load forecasting plays a crucial role in operation, planning and maintenance of power system. This study was designed to investigate the effect of deseasonalisation on electricity load data forecasting. The daily seasonality in electricity load data was removed and the forecast methods were employed on both the seasonal data and non-seasonal data. Holt Winters method and Seasonal-Autoregressive Integrated Moving Average (SARIMA) methods were used on the seasonal data. Meanwhile, Simple and Double Exponential Smoothing methods as well as Autoregressive Integrated Moving Average (ARIMA) methods were used on the non-seasonal data. The error measurement that were used to assess the forecast performance were mean absolute error (MAE) and mean absolute percentage error (MAPE). The results revealed that both Exponential Smoothing method and Box-Jenkins method produced better forecast for deseasonalised data. Besides, the study proved that Box-Jenkins method was better in forecasting electricity load data for both seasonal and non-seasonal data.

Analysis and Predictions of Seasonal Affected Weather Variables of Bangladesh: SARIMA Models vs. Traditional Models

Bangladesh is a semi-tropical country, categorized by widespread seasonal disparities in rainfall, temperature, and humidity. Seasonality has been an input aspect of time series modeling when taking into account weather variables. In terms of multiple features of the weather variables i.e. randomness, cyclical variation and trend, time series methods etc. ARIMA can be a superior preference but, weather variables are affected by seasonality. Thinking about the grimy meadow, this paper presents Seasonal Auto-regressive Moving Average (SARIMA) model that takes seasonal and cyclical variation over the years. This study also aims to compare traditional methods like Single Exponential Method, Double Exponential Method, and Holt Winter Method with the SARIMA model. Time series plots, month plots, and B-B plots are used for identifying seasonal effect clearly. For seasonal stationary checking, Canova Hansen Stationary test has been utilized. Then, the order of the variables is identified, ACF and PACF have been checked and estimated preeminent order for these variables by AIC and Log-likelihood. Finally, Single Exponential Method, Double Exponential Method, and Holt Winter Method are introduced for comparing and forecasting. The proposed models SARIMA(0,0,0)(1,0,3)12, SARIMA(0,0,0)(1,0,1)12, SARIMA(0,0,0)(1,0,2)12 and SARIMA(0,0,0)(1,0,1)12 for maximum and minimum temperature, rainfall and humidity on the basis of Akaike Information Criteria and Log likelihood have been captured most seasonality of the data. Comparing them with traditional methods, traditional methods give a better result than the acquired model based on error measurement. So, traditional methods give a better estimate than the SARIMA models for selected weather variables, with lower mean square error, RMSE, MAE and MASE.

Aplikasi Peramalan Jumlah Siswa Sekolah Dasar di Kabupaten Tanah Laut Menggunakan Metode Holt’s Double Exponential Smoothing

Learning processes in the elementary schools at Tanah Laut District is affected by the number of students. Through the number of students can be predicted how much the need of additional teachers, rooms, textbooks and learning medias that support learning processes in the schools. In other words, the infrastructure of the schools can be predicted by the number of students that is registered in Tanah Laut District. The research use Holt’s Double Exponential Smoothing method to predicting the number of the prospective students in Tanah Laut District. Mean Absolute Percentage Error (MAPE) technique is used to calculate the percentage of error from the forecasting’s result. The system is designed by Entity Relationship Diagram (ERD) and Data Flow Diagram (DFD). The forecasting that have been done said that the number of Tanah Laut’s elementary school students at 2018 is 35655 students, with the value of MAPE is about 0.77%, a = 0.77 and ß = 0.8.

IMPLEMENTASI SISTEM INFORMASI PERAMALAN STOK JENIS KOMODITI TANAMAN PANGAN DI ACEH UTARA

Commodity types of food crops are one of the sectors that are very important for food security, and this is very influential in maintaining food security in Indonesia. Food crops commodities in northern Aceh that have commodities include rice, soybeans, corn, peanuts, green beans and so on. The problem now is the difficulty of finding information which is an obstacle to analyzing the stock model of the types of food crops. This commodity plays an important role in people's lives in the next few years. There is a forecasting information system to be able to see all the information on the stock inventory report of food crop commodities in the future. The Double Exponential Smoothing model is very appropriate because the behavior of the commodity type data is seasonal (per month) and trend (increase). With annual data reaching 47716.14 tons for rice plants, for soybeans reached 6141.81 tons. Estimated forecast for January-December. On average every January 32600, February 32300, March, 31200, April 29500, May 38000, November 29500, December 35800 for the following year experienced an increase. Next to the value of Set Alpha 0.2. The period of one for the value of'tt is 32600, the value of s''t is 32600 for at is 32600, for the value of bt is 0, then in the second period of value, the'tt is 32540, the value of s''t is 32588 for at is 32492, for the value of bt is -12. Furthermore for the period 25, Shout is 35360, the value of no is 33228.94109 for at 37491.05891, for the value of bt is 532.7647285, and for the period 26 the last is Shutter is 33340, the value is 33251.15287 for at is 33428.84713, for the value of bt is 22,21178279. The next prediction result of rice commodity 33451.05 and mape error value is 0.334216418% with alpha set value 0.2.Keywords: Information System, DES, Type of Food Crop Commodity

Improved dissipation kinetic model to estimate permissible pre-harvest residue levels of pesticides in apples

Prediction of residual concentrations of applied pesticides during the pre-harvest period may be required to ensure the safety of agricultural products. In this study, time-dependent dissipation trends of carbaryl (CB), kresoxim-methyl (KM), flubendiamide (FB), flufenoxuron (FN), bitertanol (BT), and chlorantraniliprole (CN) applied to apples at recommended and threefold greater doses were modeled to estimate pre-harvest residue limit concentrations (CPHRL) indicating permissible pesticide concentrations during the pre-harvest period. Double-exponential (DE) model results best fit the dissipation trends of all tested pesticides (correlation coefficients of 0.91-0.99) compared to zero-, first-, and second-order models. Among the pesticides examined, CB half-lives in apples of 2.9 and 6.6days were the shortest, while those of FN (21.1-32.7days) were the longest. The CPHRL values for each pesticide in apples were estimated with DE model parameter values and could be used to determine harvest dates for safe apples with pesticide concentrations below their maximum residue limits. Application of the DE model for CPHRL calculation provides more accurate information for farmers to produce agricultural products safe from pesticide residues.

Location priority for non-formal early childhood education school based on promethee method and map visualization

Non-formal Early Childhood Education (non-formal ECE) is an education that is held for children under 4 years old. The implementation in District of Banyumas, Non-formal ECE is monitored by The District Government of Banyumas and helped by Sanggar Kegiatan Belajar (SKB) Purwokerto as one of the organizer of Non-formal Education. The government itself has a program for distributing ECE to all villages in Indonesia. However, The location to construct the ECE school in several years ahead is not arranged yet. Therefore, for supporting that program, a decision support system is made to give some recommendation villages for constructing The ECE building. The data are projected based on Brown’s Double Exponential Smoothing Method and utilizing Preference Ranking Organization Method for Enrichment Evaluation (Promethee) to generate priority order. As the recommendations system, it generates map visualization which is colored according to the priority level of sub-district and village area. The system was tested with black box testing, Promethee testing, and usability testing. The results showed that the system functionality and Promethee algorithm were working properly, and the user was satisfied.

Optimasi Parameter Pemulusan Algoritma Brown Menggunakan Metode Golden Section Untuk Prediksi Data Tren Positif dan Negatif

Algoritma DES (Double Exponential Smoothing) Brown merupakan algoritma peramalan yang digunakan untuk memprediksi data deret berkala baik berpola tren positif maupun tren negatif. Namun algoritma ini mempunyai kelemahan yaitu dalam menentukan nilai parameter optimum untuk meminimasi error peramalan (MAPE), nilai parameter tersebut dicari menggunakan metode Golden Section dimana sebelumnya dicari secara manual menggunakan percobaan berulang kali. Penelitian ini menggunakan 60 data berpola tren yang dianalisis untuk pengelompokan pola data tren positif dan negatif dimana selanjutnya dilakukan proses peramalan, evaluasi dan pengujian untuk mengetahui jenis pola data tren apa yang terbaik. Dari hasil perhitungan dan pengujian diketahui bahwa parameter optimasi menghasilkan nilai MAPE yang optimum, dimana selanjutnya nilai parameter tersebut dilakukan proses peramalan pada kelompok pola data tren positif dan negatif yang menghasilkan rata-rata nilai MAPE sebesar 9,73401% (highly accurate) untuk data berpola tren positif dan 15,78467% (good forecast) untuk data berpola tren negatif. Algoritma peramalan DES Brown dengan metode optimasi parameter menghasilkan nilai pendekatan terhadap data asli jika data tersebut menunjukkan penambahan atau penurunan nilai disekitar nilai rata-rata. Sebaliknya, akan menghasilkan nilai MAPE yang tinggi (tidak akurat) jika data tersebut memiliki lonjakan periode nilai data. Dari kedua kelompok nilai MAPE tersebut dilakukan uji t statistik yang menyatakan bahwa data berpola tren positif () menghasilkan nilai rata-rata MAPE lebih baik dibandingkan data berpola tren negatif (μ2).

Perbandingan Metode-metode Peramalan Statistika untuk Data Indeks Harga Pangan

The food issue is very important to predict the price of food, especially rice, in the future. This is because rice is the Indonesian staple food, the price of which must be always made stable and affordable for the community. Therefore, it is necessary to make a proper forecasting model to know the approximate price of rice in the future. The forecasting method that is often used in researches is a quantitative forecasting method with time series data. This research is conducted to make comparison among the statistic forecasting methods, namely trend analysis, exponential smoothing and decomposition to analyze the development of average rice price at Indonesian wholesaler level from 2010 to 2016. The existing data shows that the most suitable forecasting model for the average data of rice price at Indonesian wholesaler level in 2010 to 2016 is Double Exponential Smoothing (Brown), with MSE level of 188,086.086. and that the prediction about average rice price in 2017 reaches Rp. 11600,- with the actual price of Rp. 11534,-

PERAMALAN PENERIMAAN BEA CUKAI INDONESIA

The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2) regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords: Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models

Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models

Numerical solution of nonlinear two-dimensional Fredholm integral equations of the second kind using Sinc Nyström method

ABSTRACTIn this paper, a double-exponential (DE) Sinc Nyström method is utilized to solve nonlinear two-dimensional Fredholm integral equations of the second kind. Using the DE transformation, the Sinc quadrature rule for a definite integral is extended to double integral over a rectangular region. Therefore, a nonlinear Fredholm integral equation is reduced to a system of nonlinear algebraic equations, which is solved using the Newton iteration method. Convergence analysis shows that the proposed method can converge exponentially. Several numerical examples are provided to demonstrate the high efficiency and accuracy of the proposed method.

Bimodal Soil Pore Structure Investigated by a Combined Soil Water Retention Curve and X‐Ray Computed Tomography Approach

Core Ideas Soil water retention curves of the paddy soil were bested fitted with bimodal models. Computed‐tomography imaging and retention curves generated similar bimodal pore structure. Combined use of organic and inorganic fertilizers increased structural porosity. Textural porosity was not affected by fertilization treatments. Well‐structured soils are considered to have bimodal pore structure, including textural pores between particles and structural pores between aggregates. Pore structure can be inferred from the soil water retention curve (SWRC) but our understanding of the 3‐D pore geometry that regulates this curve is limited. This study investigated the pore structure of a paddy soil under different fertilization regimes by both SWRC and X‐ray micro‐computed tomography (micro‐CT) imaging with the aim of comparing the two methods. Undisturbed soil aggregates and cores were collected from the surface layer of long‐term unfertilized (CK), inorganically fertilized (NPK), and organically and inorganically fertilized (NPKOM) paddy soils. Aggregates and cores were scanned with micro‐CT and pore structures were analyzed. The SWRCs were measured on the same CT‐scanned cores. Three unimodal models, three bimodal models, and one trimodal model were evaluated for fitting the SWRC and to derive soil pore size distribution (PSD). Results showed the SWRC of the paddy soil were best fitted with the bimodal lognormal (BLN) and double‐exponential (DE) models, with the derived PSD showing distinct bimodality. The micro‐CT images revealed the hierarchy structure of the paddy soil and a bimodal pattern in the PSDs. The structural porosities from BLN, DE models and CT imaging were consistent and correlated with the natural logarithm of saturated hydraulic conductivity. Long‐term application of NPKOM increased structural porosity though no significant differences were recorded in the textural porosity compared with the NPK and CK treatment, while the latter two showed a near identical pore structure. These results demonstrated the benefits of both approaches especially for soil structures with a bimodal pore structure such as the well‐structured paddy soil.