Simple Exponential Smoothing Method Research Articles

Managing the risks against carbon neutralization for green maritime transport

Maritime transport plays a vital role in global trade. With low transport costs and high service reliability, approximately 80% of products are transported by sea. The international nature of maritime transport supports globalized production and consumption patterns. As a result, maritime transport is uniquely influenced by the convergence of environmental, geopolitical, and social factors. One of the environmental factors linked to maritime transport is emissions. Although many steps have been taken to reduce emissions within the scope of carbon neutrality by implementing new technologies and policies, the volume of emissions produced in maritime transport is still relatively high and threatens global sustainability. This research attempts to estimate the volume of carbon emissions produced by international shipping using the Simple Exponential Smoothing method. After highlighting the situation, this research aims to determine the risks that cause carbon emissions and which should be prioritized using the Graph Theory and Matrix Approach. This approach draws attention to the fact that a scientific basis has remained in the background. This study emphasizes the scientific basis of the method used to estimate carbon emissions, ensuring that geopolitical and social risks are taken into account as well as environmental factors. Thus, it offers a more comprehensive perspective to help managers and decision-makers in developing emission reduction strategies, contributing to the greening of maritime transport.

Sales and Operation Analysis: A Case Study in Pt. Berkat Popok Bahagia

The purpose of this paper is to help solve one of the current existing issues of PT. Berkat Popok Bahagia which is SKUs being out of stock. It turns out that there are several reasons for this. The first reason is the amount of the stock of each SKU in the system differs from the amount of stock in the warehouse. The second reason is the demand from the distributors is not in sync with the sales and operational planning team, making it when the distributors order products (SKU) from the factory, the product is not available because it might not be in the production process. The third reason is the forecasting method that is currently being used is less suitable. Author compared the forecasting accuracy of each forecasting method in the top 2 SKUs of baby care category from simple moving average method, simple exponential smoothing method and exponential smoothing with trend method. The results of the forecasting accuracy is based on the MAPE value in which lower value indicates better method. The forecasting accuracy measurement result when comparing the 3 methods between the 2 SKUs are respectively 24.3% and 20.24% for simple moving average, 20.28% and 16.19% for simple exponential smoothing and 18.07% and 15.65% for exponential smoothing with trend. The results indicate that exponential smoothing with trend provides the best performance among the three which therefore author recommend this method compared to the current simple moving average method.

Human Development Index Forecasting with Moving Average, Simple Exponential Smoothing and Naïve Method

The purpose of this research is to forecast the Human Development Index (HDI) using the moving average (MA) method, the simple exponential smoothing method and the naïve method. The region forecasted for its HDI is North Lombok Regency, for which it has the lowest HDI in West Nusa Tenggara, Indonesia. The data used for this research is the HDI from 2010 to 2022. The selection of these methods is due to the limited amount of data and the popularity of MA, SES, and Naïve method in the world of forecasting until this day. The results of this research is the MA method which consists of MA MA3, SMA MA5, WMA MA3, WMA MA5, EMA MA3, EMA MA5, and SES tested value α=0.1 and α=0.9 and Naïve method have a high degree of accuracy that can be seen in the Mean Absolute Percentage Error (MAPE) value which is below 10%. However, the chosen method is the best method with the smallest MAPE which is Naïve method with MAPE 1.32% where MAPE is below 10% indicating that the model used is Highly accurate and the result of North Lombok Regency HDI in 2023 is 65.7 which means that the HDI in North Lombok Regency has not changed. HDI in North Lombok Regency stay on middle level

Prediksi Deret Waktu Manajemen Lalu Lintas

Optimizing customer service for the number of drivers in an area through real-time transportation service industry online to scale up. In this paper, the dataset used is traffic management accompanied by attributes such as level 6 geohash, day, timestamp, and demand. The dataset used is a sample from geohash6 coded qp0991, containing online transportation demands from 01/04/2018 until 31/05/2018 (61 days). The training datasets are from the qp0991 code sample, starting from 01/04/2018 until 10/05/2018 and the remaining datasets are used as the testing datasets. The percentages for training and testing are respectively 67% and 33%. The methods applied to construct the objective function are three different forecasting methods, namely the Naïve approach, auto-regressive integrated moving average (ARIMA), and simple exponential smoothing. The results of this study indicate that the simple exponential smoothing method is better than the naïve approach and auto-regressive integrated moving average based on the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The simple exponential smoothing has an accuracy rate of 98.7% for the RMSE value, 98.9% for the MAE value, and 88.81% for the MAPE value.
 Keywords: time series, traffic management

Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

Short-term forecasting of salinity intrusion in Ham Luong river, Ben Tre province using Simple Exponential Smoothing method

Salinity intrusion in a river may have an adverse effect on the quality of life and can be perceived as a modern-day curse. Therefore, it is important to find technical ways to monitor and forecast salinity intrusion. In this paper, we designed a forecasting model using Simple Exponential Smoothing method (SES) which performs weekly salinity intrusion forecast in Ham Luong river (HLR), Ben Tre province based on historical data obtained from the Center for Hydro-meteorological forecasting of Ben Tre province. The results showed that the SES method provides an adequate predictive model for forecast of salinity intrusion in An Thuan, Son Doc, and Phu Khanh. However, the SES in My Hoa, An Hiep, and Vam Mon could be improved upon by another forecasting technique. This study suggests that the SES model is an easy-to-use modeling tool for water resource managers to obtain a quick preliminary assessment of salinity intrusion.
 Xâm nhập mặn có thể gây tác động xấu đến đời sống con người, tuy nhiên nó hoàn toàn có thể dự báo được. Cho nên, một điều quan trọng là tìm được phương pháp kỹ thuật phù hợp để dự báo và giám sát xâm nhập mặn trên sông. Trong bài báo này, chúng tôi sử dụng phương pháp Simple Exponential Smoothing để dự báo xâm nhập mặn trên sông Hàm Luông, tỉnh Bến Tre. Kết quả cho thấy mô hình dự báo phù hợp cho các vị trí An Thuận, Sơn Đốc, và Phú Khánh. Tuy nhiên, các vị trí Mỹ Hóa, An Hiệp, và Vàm Mơn có thể tìm các phương pháp khác phù hợp hơn. Phương pháp Simple Exponential Smoothing rất dễ ứng dụng trong quản lý nguồn nước dựa vào việc cảnh báo xâm nhập mặn.

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

service level agreement (SLA) management is one of the key issues in cloud computing. The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness. To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring. There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider. The contribution of this paper is two-fold. First, we analyzed the accuracy of six widely used prediction algorithms—simple exponential smoothing, simple moving average, weighted moving average, Holt–Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average—by varying their individual control parameters. Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks. Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones. To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1–100, 1–200, 1–300, 1–400, and 1–500. From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties.

A Comparison of Statistical Methods for Forecasting the Price of Jasmine Rice

The objective of this study is to compare five forecasting statistical methods on the price of Jasmine rice. These methods are Simple Exponential Smoothing method, Holt’s method, Holt–Winter’s method, Box–Jenkins method and combined forecast based on regression method. All methods are tested on the data collection of the monthly series of the price of Jasmine rice from January 2007 to December 2012 recorded by the Bank of Thailand. By using the mean square error (MSE) and the mean absolute percent error (MAPE), the Holt’s method is the most promising method. In addition, Government and authorities should use this result for forecasting the price of Jasmine rice.

An integrated mixture of local experts model for demand forecasting

Demand forecasting is an important issue in the supply chain planning process due to its usage for efficiently managing different planning tasks. The common techniques adopted to perform such task are easy to use forecasting methods performed in computerised systems with minimum human intervention such as the simple exponential smoothing. Concerning the drawbacks of these modelling approaches, past evidence suggests that they have been less successful in predicting short-term fluctuations on demand. In this work, the simple exponential smoothing method is employed to build an integrated mixture of local experts model (IMLEM) for demand forecasting using as independent variable a lagged gross domestic product (GDP) indicator. The IMLEM is usually employed to deal with rapidly changing situations and it is used in this work to improve the performance of the forecasts by providing a way to take into account short-term fluctuations on demand, due to changes on economic activity growth, within an easy to use forecasting method. The proposed method was applied on one-step ahead demand forecasting for a Brazilian beverage manufacturer for 130 non-seasonal stock keeping units (SKUs) and the accuracy of the generated forecasts allowed to reduce the forecasting errors. The obtained results also supported the decision making process by aiding demand planners concerning how to react to short-term fluctuations on demand.

A Study on the Travel Speed Estimation Using Bus Information

This study was conducted to investigate that bus information was used as an information of travel speed. To determine the travel speed on the road, bus information and the information collected from the point detector and the interval detection installed were compared. If bus information has the function of traffic information detector, can provide the travel speed information to road users. To this end, the model of recognizing the traffic patterns is necessary. This study used simple moving-average method, simple exponential smoothing method, Double moving average method, Double exponential smoothing method, ARIMA(Autoregressive integrated moving average model) as the existing methods rather than new approach methods. This study suggested the possibility to replace bus information system into other information collection system.

Rating non-elite tennis players using team doubles competition results

Statistical methods can be useful in rating non-elite tennis players. This paper shows how clubs can use simple optimization techniques to rate their club's players in doubles tennis competitions. Even though clubs lack all relevant information, the effects of home advantage, position played, partner and strength of team opposition can be taken into account and evaluated. The results from all competing clubs, available to the body organizing the competition, are used to rate all players in the competition, and validate the results that were obtained with limited information. We show a home ground advantage exists in non-elite doubles tennis. A simple exponential smoothing method of rating players is then tested, and shown to produce reasonable results.

A Statistical Approach to the Modified Impress Current Cathodic Protection (ICCP) for Pipeline Systems.

An attempt to predict theoretically, the corrosion protection of oil pipelines by Impressed Current Cathodic Protection (ICCP) was embarked upon using data from an ICCP system in practice for three years (2004 through 2006). The concepts and principles used in Cathodic protection is briefly explained with a discussion of the forecasting models employed. The forecasting methods employed include the Simple Moving Average Method, the Simple Exponential Smoothing Method and the Double Exponential Smoothing Method. The forecasting models were applied on the collected data and results obtained and tabulated. The forecasted results were plotted and superimposed on the actual (collected data) and observations made. The results show that the forecasted results followed closely to the actual data, confirming that forecasting results can only be relied upon in ICCP monitoring if there are no coating damages, interference problems and other anomalies. Keywords: Impressed Current Cathodic Protection (ICCP), Simple Moving Average Method, Simple Exponential Smoothing Method and the Double Exponential Smoothing Method.

Application of uncertainty reasoning based on cloud model in time series prediction.

Time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

Validation en temps réel des données des apports naturels journaliers pour la gestion des réservoirs

The main objective of this study is to propose a method for validating the daily net basin supply data to obtain the most reliable values for the management of water resource systems. Based on the regression and exponential smoothing methods, various validating procedures are developed and compared. Results of a numerical application using data from La Grande 3 basin have shown that the simple exponential smoothing method is the most suitable. Further, it has been found that the proposed exponential smoothing has the simplest structure and would require a simple parameter estimation method because it contains only one parameter.Key words: real time validation, net basin supply, forecasting, time series analysis, reservoir management.

Validation en temps réel des données des apports naturels journaliers pour la gestion des réservoirs

The main objective of this study is to propose a method for validating the daily net basin supply data to obtain the most reliable values for the management of water resource systems. Based on the regression and exponential smoothing methods, various validating procedures are developed and compared. Results of a numerical application using data from La Grande 3 basin have shown that the simple exponential smoothing method is the most suitable. Further, it has been found that the proposed exponential smoothing has the simplest structure and would require a simple parameter estimation method because it contains only one parameter.Key words: real time validation, net basin supply, forecasting, time series analysis, reservoir management.