Single Exponential Smoothing Research Articles

Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.

PERENCANAAN KEBUTUHAN TANDAN BUAH SEGAR DENGAN PERAMALAN METODE SINGLE MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING

This research examines the problem of procuring raw materials for production in a state-owned enterprise which operates in the oil palm plantation business and processing palm oil Fresh Fruit Bunches (FFB) into CPO (Crude Palm Oil) and Palm Kernel. The factory has a FFB processing capacity of 20 tons/hour, while the company experiences fluctuating supplies of raw materials so that it does not match the available factory capacity and causes loss of production time. With this problem, researchers are trying to find a more accurate forecast that is useful for meeting demand in order to increase the optimization of factory production capacity. The methods used in this research are single moving average and single exponential smoothing. These two methods will be compared by looking for the smallest error value in terms of MAD MSE and MAPE values.. The method with the smallest error value can be a proposed forecasting method for the future. The calculation results show that the single exponential smoothing method has a smaller error value.

Tomato plant production in NTB per year using Single Exponential Smoothing (SES) Method

Tomato plant production in NTB per year using Single Exponential Smoothing (SES) Method

Peramalan Kebutuhan Obat Menggunakan Metode Single Exponential Smoothing

Ogan Ilir Regional General Hospital (RSUD) tries to maintain the availability of medicines to meet patient needs. The Pharmacy Department of Ogan Ilir Regional Hospital always monitors existing drug stocks before placing orders with distributors of pharmaceutical supplies. The drug procurement process is carried out in a planned manner to reduce the risk of loss from the method of drug procurement and to identify the need for highly used drugs. This research aims to determine the forecasting of drug needs using the Single Exponential Smoothing method. The data used is drug use data for the period 2021 to 2022. Based on the research results, it was found that based on the highest MAPE score it was at 22% for alpha 0.1, and the highest MAPE was at 12% for alpha 0.5. These results show that the prediction of drug use is successful and has good value because the MAPE score does not exceed 20%.

Demand forecasting under lost sales stock policies

Demand forecasting is a crucial task within supply chain management. Stock control policies are directly affected by the precision of probabilistic demand forecasts. For instance, safety stocks and reorder points are based on those forecasts. However, forecasting and replenishment policies have typically been studied separately. In this work, we explore the influence of inventory assumptions on the selection of the forecasting model. In particular, we consider when the stock policy follows a lost sales context and the demand is estimated by means of sales data. In that case, forecasting models should use censored demand estimations. Unfortunately, the literature about censored demand forecasting remains very limited, without an accepted general solution for this problem. In this work, we bridge that gap by proposing the Tobit Kalman filter (TKF). To the best of our knowledge, this is the first time that the TKF has been applied to supply chain demand forecasting, and this approach may represent a general solution for lost sales contexts. The TKF is compared with a previous ad hoc censored demand forecasting solution that is based on single exponential smoothing. In addition, we show the performance of the TKF when dealing with trends where ad hoc approaches are not available for use as benchmarks. To express the potential benefits of the proposed approach in terms of costs and the service level, a newsvendor stock policy is employed. Simulated demand data and a case study are used to illustrate the significant advantages of the proposed tool.

Implementasi Metode Time Series Dalam Forecasting Penggunaan Satusehat

Since the Covid-19 pandemic occurred in Indonesia, the government has made new innovations in the field of technology to help the government and society monitor the spread of Covid-19 in Indonesia. This innovation is the PeduliLindungi application, which has tracking and other features that people always use. The influence of PeduliLindungi has become an obligation for the Indonesian people, but since the endemic, the PeduliLindungi application has changed to the SatuSehat application which functions to integrate Indonesian people's health data. The change in the application from PeduliLindungi to SatuSehat reduced the level of application usage, so this research aims to determine the enthusiasm of the public in using the SatuSehat application as a health platform provided by the government in Indonesia. The research was carried out by predicting the number of users of the SatuSehat application based on user data from October 2022 to February 2023. The forecasted data determined the number of users in the next seven months. The research used the Time Series method with a Single Moving Average (SMA) and Single Exponential Smoothing (SES). The research results are in the form of forecasting calculations used to determine the level of enthusiasm of the community in accepting and using the SatuSehat application. After calculations, it was discovered that the research results showed that SatuSehat application users had decreased from March 2023 – September 2023. Error Forecasting showed that the accurate method for forecasting calculations for SatuSehat application users was the SMA method, which obtained MAD calculation results of 1,895,828,857 and MAPE 8.66%.

SUGAR DEMAND FORECASTING IN PT XYZ WITH WINQSB SOFTWARE

PT XYZ is a manufacturing company engaged in the production of sugar and its by-products. Currently, the determination of the amount of production at PT XYZ has not been adjusted to meet customer demand, which may continue to decrease or increase for each period. If there is a condition that the amount of production is greater than demand, it will increase the cost of storage due to accumulation. Meanwhile, if the amount of production is smaller than demand, there will be an out-of-stock condition that can reduce consumer confidence. These problems can be solved by forecasting Tambora Sugar demand at PT XYZ to meet consumer demand using the forecasting method (forecasting) with the help of WinQsb software with input, namely sugar demand data from 2021 PT SMS. The request data will later be analyzed from the request using a scatter diagram. Furthermore, after the pattern is known, the appropriate forecasting method will be determined and inputted into the WinQsb software. Based on the calculation results, it is known that the demand pattern from last year tends to trend down so the chosen method is the Double Exponential Smoothing (DES), Single Exponential Smoothing (SES), and Linear Regression (LR) method, with the best method being Linear Regression which produces the smallest error. The output is in the form of a Master Production Schedule (MPS), namely in the 13th to 18th periods, respectively 2142; 1757; 1373; 989; 604; 220 sacks.

Penerapan metode Single Exponential Smoothing untuk memprediksi permintaan labu darah

The Indonesian Red Cross (PMI) Blood Transfusion Unit (UTD), which is tasked with meeting the need for blood bags in Malang City is experiencing several problems. One of them is that the supply of blood bags is less than the demand for blood. Based on administrative data, demand for blood gourds in PMI Malang City will increase in 2022 compared to 2021 and 2020. To overcome limited supplies, PMI needs to make estimates to anticipate a spike in demand for blood bags. This research aims to determine the future demand for blood bags, or what is called forecasting. One method of time series forecasting is Single Exponential Smoothing. The data used is the demand for blood bags in 2020-2022 at PMI Malang City. To measure the error level of the forecasting model, this research uses Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The best MAPE value of 7.75% was obtained at α=0.5. The MAPE value can be categorized as very good because it is less than 10%. And based on MAE measurements, the best value is also obtained at α=0.5.

ANALISIS PERAMALAN PENJUALAN AYAM DENGAN MENGGUNAKAN METODE MOVING AVERAGE DAN EXPONENTIAL SMOOTHING DI CV. TABASSAM AZ ZUFAR JOMBANG

In this modern era of the technology, the rapid development of the business world and the demand of product that required by consumer is very high also. Therefore, the company must be able to make an optimal planning, there is in the planning of production or planning of demand to make the company can achieve the objectives targeted. To achieve it, the company have to forecast the amount of selling or consumer’s demand about product or services produced. The CV. Tabassam Az-Zufar Jombang is a company that engaged in the partnership of boiler chicken farms. One of analysis that can be used to forecast is the moving average and the exponential smoothing method. The aims of this research are the first is to know the chicken sales forecasting in the periods of June 2018 until July 2018 and the second is to know the accuratest of the sales forecasting method. The method that will be used to this research is the moving average and exponential smoothing method. The result of the research showed that the smallest error value of method is SES method with α = 0,7 where is the value of MAD is 23.384, MSE = 1.096.023.269, and MAPE = 16 %. As for result of the chicken sales forecasting using the single exponential smoothing method with α = 0,7 in June 2018 is 207.758 tails and July is 207.758 tails.

Comparison of SES and SMA Method Against Production Level Property of Fabrication Precision Engineering and Its Effect on Production Planning (Case Study PT X)

One of the goals of forecasting is to be able to predict the data needed in the future, one of which is the data on demand for the amount of production in a company. PT X is a manufacturing company based on demand or custom. This leads to uncertainty in the use of required materials, so proper forecasting is needed to estimate the material stock requirements. This study used single exponential smoothing and single moving average methods with quantitative approaches to aluminum materials. By calculating forecasting using these two methods, it is possible to find the best method for use by PT X. Based on the test, the SES method has the smallest error rate so it can be used to analyze the data, with α=0.8 yielding a forecast in the 13th month of 308.71408 pcs.

Effectiveness of Single and Double Exponential Smoothing: SES, ARRSES and Holt’s Linear for Time Series Data Prediction with Trend and Non-seasonal Characteristic (Covid-19 Vaccinate Case)

Purpose of this research is effectiveness the exponential smoothing for predict the time series data which has trend and non-seasonal characteristic. In this research using data case vaccinate of covid-19 1st, 2nd, 3rd and 4th. The advantage of this research is we can choose the best method for this type data. The methodology of this research is quantitative, with analyze data method is exponential smoothing (SES, ARRSES, and HOLT’S linear). The result of data analyze is the predict error for four vaccinate data are very close to zero. SES gets 0% to 0,73%, ARRSES gets 0% to 0,66% and Holt’s linear gets 0% to 0,29%. The result of this research can conclude that exponential smoothing method effective for predict the data with trend and non-seasonal data.

PENERAPAN METODE SINGLE EXPONENTIAL SMOOTHING DALAM MEMPREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU

Forecasting is a technique for estimating future data by looking at past data and current data. Admissions of new students at Tribuana Kalabahi University are carried out every year since the founding of the Tribuana Kalabahi University campus, in 2007, the number of new student admissions from year to year has experienced an upward trend, therefore forecasting is carried out using Single Exponential Smoothing. The amount of data from 2007 to 2022 is 16 data. The calculation results with Single Exponential Smnothing have the lowest MAPE value, namely 1.41005% with a value of α = 0.1, the number of new student admissions is 664 people

Building The Prediction of Sales Evaluation on Exponential Smoothing using The OutSystems Platform

To get a large profit in a company or business is to determine sales predictions for the next period. Prediction or forecasting is one of the keys to the success of sales because the predicted value of sales can be used as a reference to determine the order of goods, so there is no loss. Exponential smoothing method is a fairly superior forecasting method in long-term, medium-term and short-term forecasting. The data to be processed is sales data for the 2020-2022 period. The single exponential smoothing method was chosen because it can determine sales predictions for the next period with the smallest error value. The evaluation method used is MAPE, ME, MAD and MSE where this forecasting method is used to find the smallest error value. Based on the calculation results, the smallest error value obtained is ME at 62.8, MAD at 179.9, MSE at 55564.5, and MAPE at 9.20%. The value is at alpha 0.3. The next stage is to design a prediction system using the out-systems platform version 11.14.1 as a place to design the system. The test results of the system that has been designed to assist business owners in making decisions on product inventory estimates.

Forecasting of Resin Yield and Number of Blazes of Naturally Regenerated Chir Pine (Pinus roxburghii Sargent) in Himachal Pradesh by Using Single Exponential Smoothing Method

The present investigation entitled “Forecasting of resin yield and number of blazes of naturally regenerated chir pine (Pinus roxburghii Sargent) in Himachal Pradesh by using single exponential smoothing method” was carried out during the year 2020-2022. The secondary information was collected regarding the resin yield and number of resin blazes w.e.f. 2005 to 2022 from Himachal Pradesh State Forest Department. Exponential smoothing is a particular moving average technique applied to time series data and to produce smoothed data to make forecast. In exponential smoothing, one or more smoothing parameters are to be determined explicitly and those choices determine the weights assigned to the observations. Forecasting with the help of various linear and non-linear models is on the assumption that the series is stationary. Often time series is found to be non-stationary which means they are integrated and can be made stationary by differencing the time series. To check the stationarity of the number of blazes and resin yield, Augmented Dickey Fuller test was used. The results indicated that data was not approaching stationarity even after taking third difference. So, the prerequisite condition of ARIMA model is that the data should be stationary and if the time series data does not contain trend and seasonal components, Single Exponential Smoothing model was used. The Single Exponential Smoothing model was found to be best fit for the prediction of number of blazes and resin yield as per the high value of , low value of MAPE and Normalized BIC.

SISTEM PERAMALAN PENJUALAN MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING BERBASIS WEB DI PT. SANGHIANG PERKASA

Nutrition Industry, does not have a stock prediction system. So far, PT Sanghiang has relied solely on the warehouse manager's estimates to make decisions about inventory for the next period. This is a problem because stockouts often occur, and sometimes there are insufficient supplies to fulfill orders. Therefore, to avoid such issues, there is a need for a system that can help predict stock levels and make informed decisions in planning future sales strategies. The objective of this research is to develop a web-based sales forecasting system for PT Sanghiang Perkasa and implement the Single Exponential Smoothing method in the forecasting system. The results of this research indicate that the Single Exponential Smoothing method achieves the best forecasting accuracy with an alpha value of 0.2 and a MAPE (Mean Absolute Percentage Error) of 4.01%. The sales forecast for Fitbar Choco Delight 22 G Multigrain in January 2023 is 121,741. Therefore, this method is considered highly accurate as it has the lowest MAPE value.

Prediksi Penerimaan Siswa Baru dengan Metode Single Exponential Smoothing Melalui Metrik Evaluasi MAD, MSE dan MAPE

Finding a solution to the issue of predicting the quantity of new student admissions is the goal of this study. Forecasting new student admissions is crucial for an educational institution to maintain sustainability and success. At the moment, sustainability efforts in educational institutions are mostly dependent on the activity of forecasting new student intakes. The single exponential smoothing method is one strategy used in the new student admissions process, particularly at PKBM Tahfidz At Tamam, to forecast the number of students whom would be admitted in the following year. A Pekanbaru private school named PKBM Tahfidz At Tamam places a strong emphasis on the memorizing of the Qur'an by its students in order to create a generation that is knowledgeable about both the Qur'an and the Sunnah. In order to make predictions based on historical data, the applied method makes use of statistical techniques that involve mathematical equations. The number of students who will be admitted in the following year is predicted using information on new student admissions from prior years. The error margin is calculated using a number of metrics, including MAD (Mean Absolute Deviation), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error). According to the study's findings, the single exponential smoothing method yields reliable forecasts for PKBM Tahfidz At Tamam's anticipated intake of new students the next year. Using this approach, the institution may better decide how to allocate resources, establish the curriculum, and maintain the facilities to meet the demands of potential students. As a result, the single exponential smoothing is critical to projecting the number of prospective students enrollments.

Mixed [formula omitted] control for discrete-time periodic Markov jump systems with quantization effects and packet loss compensation

This article concentrates on the issue of the mixed H2/H∞ state feedback control for discrete-time periodic Markov jump systems with quantization effects and packet loss compensation. Considering that the communication channel is limited, the state signals need to be quantized by a logarithmic quantizer with mode-dependent property before communication. When the quantized state is passed to the controller, the stochastic packet loss phenomenon may occur, which follows Bernoulli stochastic distribution. Therefore, a new packet loss compensation strategy is put forward, which adopts the single exponential smoothing method to predict the lost data. Additionally, considering that the system modes are not always fully available, a periodic controller with partially mode-dependent property is devised to ensure that the system is stochastically stable with the predefined mixed H2/H∞ performance. Then, the sufficient conditions for the existence of periodic controller are established by constructing a periodic Lyapunov function, which is mode-dependent. Ultimately, a practical example of a boost converter is presented to verify the validity of the proposed approach.

Application of the Single Exponential Smoothing Method For Flood Disaster Prediction

The country of Indonesia is seen as one that is particularly vulnerable to natural catastrophes as well as calamities brought on by human activity. A disaster that may be brought on by both natural and human sources is a flood. Disasters brought on by flooding are unpredictable occurrences that frequently cause losses in the form of property damage, the theft of assets, and lost productivity at work and in school. Through this prediction information system, the people can find out the level of risk of flooding through excessive rainfall. in order to better anticipate and prepare for all possibilities that occur before the flood, the method used is Single Exponential Smoothing. This method was chosen because of the simple way the system works to find predictive values ??through past data. With this system, researchers can input rainfall data taken from the Meteorology, Climatology and Geophysics Agency, then the data is processed through the system and if rainfall gets high results. The risk of flooding will also be very high and a warning will be given to the public so that better prepared for the risk of flooding. The results obtained from this study are the results of an analysis of the exponential method single to obtain accurate rainfall prediction information with data MAD, MSE and MAPE.

Sales forecasting of marketing using adaptive response rate single exponential smoothing algorithm

Micro, small and medium enterprises (UMKM) is one of the important aspects to support the improvement of the economy in Indonesia. Zee Mart’s business is one of the UMKM shop in Pematang Siantar City with sales and purchase transaction activities for supplies. The purpose of this study is to predict the sales of Zee Mart store goods in the coming month using the adaptive response rate single exponential smoothing (ARRSES) method. ARRSES is a method with the advantage of having two parameters, alpha and beta, where alpha will change every period when the data pattern changes. The dataset obtained will be pre-processed through data selection, cleaning, and transformation. The best beta is determined based on the level of accuracy calculated using the mean absolute percentage error (MAPE). Model development using the ARRSES method will produce forecasting percentages and errors for each product using MAPE. The number of sales data is 23,092 before preprocessing and 23,021 after pre-processing, with the total quantity of goods sold being 149,764 of 1,492 products. The results obtained using sales data 23,021 show the lowest MAPE value of 9.85 at the best beta of 0.6 with the highest accuracy of 90.15% and the model is implemented into a web interface.

Multi-step CNN forecasting for COVID-19 multivariate time-series

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.