Research on Olympic Medal Distribution Prediction Based on Exponential Smoothing, Frequency Probability, and Logistic Regression Models

BackgroundRed blood cells (RBCs) infusion is very important for the treatment of hematology patients, but how to maintain a balanced state between the supply and demand of RBCs is still a major challenge.ObjectiveThis study aimed to explore the feasibility of seasonal autoregressive integrated moving average (SARIMA) model and exponential smoothing (ES) model in predicting the clinical demand of RBCs for hematology patients each month.MethodsOur study collected the monthly RBCs usage data of hematology patients from January 2014 to December 2023 to establish the SARIMA model and ES model, respectively. Then, the optimal model was used to forecast the monthly usage of RBCs from January to June 2024, and we subsequently compared the data with actual values to evaluate the prediction effect of the model.ResultsThe best fitting SARIMA model was SARIMA (2,1,0)(1,1,1)12, whose R2 = 0.603, MAE = 37.092, MAPE = 13.693, BIC = 7.896. The best fitting ES model was Winters addition model, whose R2 = 0.702, MAE = 32.617, MAPE = 12.138, BIC = 7.485. The mean relative errors of two models were 0.085 and 0.159, respectively. The SARIMA (2,1,0)(1,1,1)12 model performed better in prediction.ConclusionCompared with the ES model, the SARIMA model has a smaller mean relative error in predicting RBCs usage in hematology patients. DM test also verify this result. But in the future, more similar research data are needed to make research more convincing.

An experimental comparison of exponential and adaptive smoothing forecasting models using actual operating data

The purpose of this study is to demonstrate how to make air quality forecasting to predict the Nitrogen Dioxide quality index in the future. In this paper, we demonstrate exploratory data analysis and compare the performance of the Autoregressive Integrated Moving Average and Exponential Smoothing Model. We used R Language and R Studio to integrate all the datasets, exploratory data analysis, data preparation, performing Autoregressive Integrated Moving Average and Exponential Smoothing methods, model evaluation, and visualization. This study used data from the automatic remote air quality-monitoring station located in an urban area in Madrid, Spain. The dataset in the period from 1 January 2001 to 31 December 2017. The dataset recorded six pollutants such as Nitrogen Dioxide, Particulate Matter 10 micrometres, Sulphur Dioxide, Carbon Monoxide, Ozone and Particulate Matter 2.5 micrometres. In this study, we focus only on Nitrogen Dioxide pollutants. From our model, we saw that exponential smoothing has better accuracy compared to the Autoregressive Integrated Moving Average. We also exposed that Nitrogen Dioxide pollutant shows unhealthy for sensitive group’s level in November to March and has the lowest level in June and July.

Money currency availability in Bank Indonesia can be examined by inflow and outflow of money currency. The objective of this research is to forecast the inflow and outflow of money currency in each Representative Office (RO) of BI in East Java by using a hybrid exponential smoothing based on state space approach and calendar variation model. Hybrid model is expected to generate more accurate forecast. There are two studies that will be discussed in this research. The first studies about hybrid model using simulation data that contain pattern of trends, seasonal and calendar variation. The second studies about the application of a hybrid model for forecasting the inflow and outflow of money currency in each RO of BI in East Java. The first of results indicate that exponential smoothing model can not capture the pattern calendar variation. It results RMSE values 10 times standard deviation of error. The second of results indicate that hybrid model can capture the pattern of trends, seasonal and calendar variation. It results RMSE values approaching the standard deviation of error. In the applied study, the hybrid model give more accurate forecast for five variables : the inflow of money currency in Surabaya, Malang, Jember and outflow of money currency in Surabaya and Kediri. Otherwise, the time series regression model yields better for three variables : outflow of money currency in Malang, Jember and inflow of money currency in Kediri.

A single prediction method has its own advantages and disadvantages in different aspects. In order to improve the accuracy of water level prediction, a water level prediction method combining The Autoregressive Integrated Moving Average (ARIMA) Model, Exponential Smoothing (ES) model and Long Short-term Memory (LSTM) model through nonlinear programming genetic algorithm is proposed in this paper. By combining the advantages of local search of nonlinear programming and global search of genetic algorithm, this method uses nonlinear programming genetic algorithm to allocate the weights of ARIMA model, ES model and LSTM model, and obtains the final water level prediction result by weighting. The empirical results show that this method not only has higher prediction accuracy than single model, but also has higher prediction accuracy than using nonlinear programming or genetic algorithm to allocate weight.

Background: In Malaysia, residential electricity consumption has shown a steady upward trend year by year. Due to this increase in energy consumption, it is important to forecast the value of electricity consumption until the year 2032 to accommodate the electricity demand. Methods: Three exponential smoothing models were compared to identify the most appropriate model in forecasting electricity consumption. The three exponential smoothing models are Simple, Holt, and Brown exponential smoothing. To identify the most appropriate model, a mean absolute percentage (MAPE) was chosen. Results: The results show that Holt’s exponential smoothing has the best performance with the lowest MAPE score of 2.299. Conclusions: Consequently, it was found that electricity consumption would substantially increase from 2647 ktoe (kilotonne of oil equivalent) to 3873 ktoe within the period of 2019 to 2032.

Starch-containing foods such as bread, pastries, and cakes are usually baked at a moderately high temperature in an oven. When these products are later exposed to room temperature, the associated gelatinized starch begins to harden which causes retrogradation and molecular realignment. Due to this circumstance, manufacturers need to have a fairly accurate estimate of products demand in order to determine the precise amount of baking powder and additives for use in their production so as not to incur losses in their business arising from the stale and consequentially unsalable products. This research was therefore focused on selecting the best forecasting model using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study. The study was based on 24-week operational period sales data collected from the company. The moving average model and the exponential smoothing model were the two forecasting models considered in this research. The data obtained was thoroughly reviewed and the results of the forecasting models were compared. The most effective model was the exponential smoothing model as it produced the lowest mean absolute percentage error on the average of 3.7347 for the cumulative days of sales under review as against the 15.1713 for the moving average model. However, the exponential smoothing model was considered the best forecasting model for minimizing forecasting error in this study.

PurposeThe purpose of this study is to develop a mathematical model that can be used to forecast the number of individuals who enter the library as well as the number of patrons that seek assistance at the reference desk of the library. An accurate estimate of demand at the reference desk is valuable for effective staffing decisions.Design/methodology/approachAn exponential smoothing model (Winter's model) was developed for forecasting. Data were gathered at the University of Tennessee at Chattanooga for an entire year. Using these data, an exponential smoothing model was formulated for forecasting the number of patrons seeking assistance. Since the data showed no trend but the presence of two seasonality factors, one for the week‐of‐the‐semester effect and one for the day‐of‐the‐week effect, the Winter's method appeared to be best suited. The Winter's method develops a formula from the data and allows the formula to be continuously fine‐tuned as new observations come in day after day.FindingsThe modified Winter's exponential smoothing model proved to be a good predictor of the number of patrons seeking assistance. In spite of large natural random variability present in the data, the actual values seem to follow the forecasts very closely.Originality/valueIt is vital to be able to forecast the number of clients at the reference desk that seek assistance per day. The modified exponential smoothing model is a valuable tool for such forecasting.

Household saving rate is an indispensable reference for people to grasp household economic behavior and analyze macroeconomic trends, and has a significant impact on capital accumulation, consumption and policy design. Taking UK household saving rate as the research object, this study compares the forecasting ability of Autoregressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model systematically based on the quarterly data from 2007Q1 to 2025Q1. Due to the economic fluctuations shown in UK from 2007Q1 to 2025Q1, it is particularly valuable to identify a robust model to forecast it. The empirical results show that the ETS (M, N, M) model is more robust than ARIMA (1,0,0) model in terms of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in out-of-sample forecasting. The in-sample fit of ARIMA model is better, but it may be over-fitted. This study can provide a reliable basis for the model selection of forecasting UK household saving rate, and the methodological approach and research conclusions of this study are also available for other similar countries.

Heavy metal contamination in mining areas has the features of time series, so it can be predicted with exponential smoothing model. On the basis of 1995–2007 monitoring data of Copper (Cu) at the surface water monitoring point 500m to the downstream of the sewage outlet in a copper ore, with cubic exponential smoothing method, the predicting model of heavy metal Cu is established through selecting different smoothing coefficient a. After applying the predicting model to predicting the content of Cu in the wastewater 500m to the sewage outlet from 2005 to 2007, it is found that the error of comparing the predicted result with correspondent actual monitoring values is less than 5%, which satisfies the requirements after testing. The predicting result shows that the mining area will be still in the contamination of Cu in the future.

Local Functional Exponential Smoothing

Having a general idea of the nature of the forecast and understanding the general methodology of forecasting in general, it is advisable for analytical departments, economic departments, management units of enterprises, banks, firms (of any socio-economic objects) to develop at least short-term forecasts based on indicators of their activity in modern conditions coopetitions to understand trends in changes in these indicators. As a result of the conducted research, the basic principles that must be observed when developing forecasts are revealed, as well as a detailed description and features of statistical auto-projective forecasting methods are given: random walk models containing a free term or it; models characterizing a deterministic trend with random fluctuations around the trend; moving average models; exponential smoothing models using simple exponential smoothing, linear, quadratic and seasonal smoothing (Brown, Holt, and Winters models); integrated presentation of autoregressive models and moving average models (parametric ARIMA models). In addition, the conducted research confirmed that the significance of the developed forecast and the level of confidence in the obtained future values of the indicators depends on the quality of the developed models. The criteria for checking the quality of the developed forecasts are: the Akaike information criterion, which evaluates the quality of the model compared to each other; the Hannan-Quinn Criterion information criterion, which is used to compare models with a different number of parameters and is an alternative to the Akaike information criterion; the Schwarz-Bayesian information Criterion, which compares the quality of a model relative to each other using a likelihood function; the mean squared error value, the absolute value of the mean squared error and the absolute value of the mean squared error expressed as a percentage.

The article is devoted to the development of an intelligent system for forecasting demographic changes, which is an important task in the modern world. Traditional analysis of demographic data faces difficulties such as limited access to up-to-date information and a significant amount of unstructured data. The system automates the processing of demographic indicators (fertility, mortality, migration) and their structuring for further analysis and forecasting. The authors have implemented ARIMA and Exponential Smoothing models that allow forecasting population size based on trends and seasonality. Testing of the models for Ukraine, Brazil, and other countries showed that the accuracy of the forecasts depends on the socioeconomic characteristics of each country. ARIMA has proven to be highly accurate in forecasting for stable regions, while Exponential Smoothing adapts to changes in trends. This system provides analysts and governments with an important tool for making informed strategic decisions in the field of population policy, allowing them to take into account complex interrelationships and dynamic trends.

One of the most important challenges when analyzing and forecasting the time series is the stability of the series and determining components of the time series such as trend and seasonal. Exponential Smoothing methods can be thought of as peers and alternatives to Box-Jenkins ARIMA class of time series forecasting methods, but the most important aspect of the exponential smoothing approach is that the time series does not have to be stable. The study introduces reviewing and comparing a variety of Exponential Smoothing models; Simple Exponential Smoothing (SES), Holt’s Linear Exponential Smoothing or Double Exponential Smoothing (DES) and Holt Winters Algorithm or Triple Exponential Smoothing (TES). Additionally; creation temporal patterns to forecast the monthly stock returns of the Saudi Stock Index by using a variety of Exponential Smoothing models. The results of the study concluded that the Holt Winters Algorithm or triple exponential smoothing model is the best model since it produces the lowest Mean Absolute Percentage error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD) values which are 4,380, 244783 compared to 4, 385, 246837 for SES, and 5, 410, 270734 for DES, and thus can be used to predict the monthly stock returns of the Saudi Stock Index.

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different iś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.