Brown's Exponential Smoothing Research Articles

Моделирование ожидаемого предотвращенного ущерба в энергетике. Часть 1. Детерминированный подход

Inspection control and supervisory activities are one of the important mechanisms of proactive industrial safety. Corporate control ensures early detection of security violations and their prompt elimination to prevent accidents. The expected prevented damage at hazardous production facilities is considered one of the efficiency factors of inspection activities. In order to assess the expected prevented damage (direct or indirect), the number of expected incidents and damage for each event must be calculated. An approach to the assessment of the number of prevented events based on the pyramid of incidents (Heinrich’s pyramid, Berd’s pyramid) by supplementing it with the level of detected violations has been proposed. The number of prevented incidents can be assessed within the framework of deterministic and scholastic approaches. The study focuses mainly on building and analysis of the ratio of the number of incidents between different levels of the pyramid within the framework of the deterministic approach. The accident statistics at oil and gas industry facilities has been analyzed; Heinrich’s hypothesis regarding the type of dependence between the pyramid levels has been tested; and coefficients of the injury rate pyramid have been determined. To find the final coefficients of the improved pyramid of an oil and gas company, the method of coefficient calculation with accumulations for each year has been chosen. It has been demonstrated that traditional values of coefficients of Heinrich’s pyramid and those obtained as a result of calculations do not comply, which can be explained by the significant difference in industrial technologies and efficiency of occupational safety measures as of today and at the moment of initial building of Heinrich’s pyramid. Based on the regression analysis and other forecasting methods (the Holt method, the Brown exponential smoothing method, the ARIMA model, and the moving average extrapolation method), modeling and forecasting injury rate factors have been conducted. The comparative analysis of methods to forecast the number of accidents has been carried out. It has been shown that it is advisable to apply the linear regression model to predict injury rates.

Forecasting electricity consumption of Malaysia’s residential sector: Evidence from an exponential smoothing model

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.

Adaptive R-Peak Detection Algorithm Based on Brown Exponential Smoothing Model

ECG is one of the most effective medical tests for heart disease diagnosis, and R-peak detection is the first step in ECG interpretation. For wearable ECG signals, the difficulty of R-peak detection mainly lies in the interference of dynamic strong noise, and the limited hardware computational resources limit the use of some complex algorithms. Therefore, an adaptive threshold R-peak detection algorithm based on Brown’s exponential smoothing model is proposed in this paper. The algorithm selects features based on the morphological characteristics and occurrence law of R-peaks, updates the threshold parameters using the Brown exponential smoothing model, optimizes the smoothing coefficients in it using the relative error least squares method, corrects the smoothing ability of the observation error and the response speed to the change of the observation, so that the updated threshold can be more consistent with the R-peak detection. Finally the algorithm achieves 99.6% precision, 99.7% recall and 99.65% F1 score on the self-constructed ECG dataset, and compares with other R-peak detection algorithms to determine the superiority of the proposed algorithm in some performance metrics. It is demonstrated experimentally that the algorithm can adapt well to the strong noise environment and obtain satisfactory R-peak detection accuracy.

Modelling the Number of People per Physician, Nurse, and Midwives in Turkey in Terms of Reproductive Health Indicators.

Health worker density and distribution is critical for a strong health system and therefore has been listed among 1 of the Sustainable Development Goal (SDG) targets. The present study aims to model the number of persons per physician, nurse, and midwives in Turkey until 2030 and to make estimates for better reproductive health outcomes. We used time series of people per physician, nurse, and midwife between the years 1928 and 2018. Estimates were obtained via the Box-Jenkins and Brown Exponential Smoothing Methods. The results of this study showed that both designed models provide a high diagnostic value to predict the number of person per doctor, nurse, and midwives. The goodness of fit criteria for both models was statistically significant. The results predict a slight decrease in the number of people per physician, a more significant decrease in the number of people per nurse, but no decrease in the number of people per midwives until 2030. We argue that there will not be much progress in reproductive health indicators if the health workforce progresses with the same trend in the coming years. We recommend decision-makers to re-consider the health workforce planning, especially in terms of the number of the person per nurses, for better reproductive health outcomes.

Small ruminant population in India - a forecast for the future

Small ruminants continue to assure subsistence by ensuring reasonable livelihood to resource poor farmers of rural India, even when the monsoon fails. A study was made to forecast the small ruminants population in the future by taking into account their past and present growth trends. Data on small ruminants population from 1950-51 to 2016-17 were collected from various reports of Basic Animal Husbandry Statistics (BAHS). Time series forecasting models were employed to identify the growth patterns and to predict the future trends in small ruminants population. Different forecasting models were compared to identify the best fit model using different criteria. From the results, it could be discerned that the Brown Exponential Smoothing model was the best fit model for forecasting sheep population and the forecasted sheep population for the years 2020-21, 2030-31, 2040-41 and 2050-51 which were 65.774, 72.673, 79.572 and 85.782 million, respectively., The sheep population would continue to exhibit positive growth in the future, due to the increasing demand for mutton owing to the increasing per capita incomes and rapid urbanisation. ARIMA (1,1,0) model was found to be the best fit model for forecasting goat population and the forecasted goat population were 139.812, 153.155, 166.401 and 178.321 million during 2020-21, 2030-31, 2040-41 and 2050-51, respectively, which showed that goats will continue to multiply faster due to high market demand.

Forecast accuracy of COVID-19 spread dynamics in Peru

Objective: To analyze the forecast accuracy of Brown's exponential smoothing model to predict the spread of COVID-19 in Peru from March 6 to May 30, 2020. Materials and methods: A descriptive study based on a time series analysis conducted from March 6 to May 30, 2020 in Peru. The information on the number of positive cases of COVID-19 (155,671 people) was used. The prediction method was Brown's exponential smoothing model, which consists in carrying out two exponential smoothings from which the forecast is calculated: the time series values were used in the first smoothing, and the first attenuation series was used in the second one. Accuracy measures used in the research were: mean forecast error (MFE), mean squared error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The coefficient of determination (R2) was used to establish if the data fits the evaluated model. Results: MFE was 156.7, MSE was 506461.3, MAD was 450.6 and MAPE was 9.03 %. R2 accounted for 0.8078. Conclusions: Accuracy error or MAPE was 9.03 % and R2 was 0.8078, which indicates that the data fits by 80.78 % to the evaluated model.

Forecasting cattle and buffalo population in India – A time series analysis

Cattle and buffalo are known to be the symbol of wealth in rural India from time immemorial. To ensure the attainable socio-economic benefits from cattle and buffalo reaching the rural poor through effective strategies, a more complete extrapolation of cattle and buffalo population in the country for the future using forecasting tools was attempted in this study. Data on cattle and buffalo population from 1950-51 to 2016-17 were collected from various reports of BAHS and FAO. Various time series forecasting models were employed to identify the growth patterns and to predict the future trends in bovine population. The forecasting models used were compared to identify the best fit model. From the results of the study, it could be discerned that the Damped Trend Exponential Smoothing was found to be the best fit model for cattle population and forecasted value indicated that the cattle population in the country would be almost stagnant in the next three decades. The cattle population forecast showed a slightly decreasing trend from 2010-11 with 194.184 million to 187.661, 188.177, 188.191, and 188.192 millions in 2020-21, 2030-31, 2040-41 and 2050-51, respectively. However, in case of buffaloes, the Brown Exponential Smoothing model was found to be the best fit model and the buffalo population was predicted to increase from 2000-01 and the predicted populations were 116.663, 127.787, 138.910 and 148.921 million in 2020-21, 2030-31, 2040-41 and 2050-51 respectively.

Forecasting Banana Harvest Area and Production in Turkey Using Time Series Analysis

In this study, 1961–2015 period FAOSTAT data of banana harvest area and production in Turkey was modeled with the objective to forecast banana harvest area and production for the 2016–2025 period. Stationarity was provided with taking the first difference of the time series. Several Autoregressive Integrated Moving Average (ARIMA (0,1,1), ARIMA (1,1,0) and ARIMA (1,1,1)) and Exponential Smoothing (Holt, Brown and Damped) models were tested. Brown exponential smoothing model was determined as the most suitable one for forecasting banana harvest area and production. Banana harvest area was forecasted to be 6175 ha in 2016 and increase to 9733 ha in 2025 year. Banana production was predicted to show a substantial increase for the 2016–2025 period, from 291,667 to 482,093 t. Briefly, the results of this study could help policy makers to develop better macro-level policies for food security and sustainability, as well as to establish better banana planting area and production strategies in Turkey for the future.

Forecasting the number of individual per dentist in Turkey; Comparison of Box-Jenkins and Brown Exponential Smoothing Estimation Methods

Forecasting the number of individual per dentist in Turkey; Comparison of Box-Jenkins and Brown Exponential Smoothing Estimation Methods

Modeling and Forecasting Meat Consumption per Capita in Turkey

The objective of this study is to model the per capita consumption of red meat in Turkey employing various time series methods, evaluate the forecasting capability of the developed models, and address the variables that may affect the per capital consumption of red meat using the cointegration method on a term basis (short/long). The material of the study consists of the per capita consumption of red meat, total annual population, feed prices, gross domestic product and share of agriculture in gross domestic product in Turkey between 1993 and 2017. ARIMA (0,1,0) and Brown's exponential smoothing method were employed to model the time series data for per capita consumption of red meat, and Johansen method was used to address the cointegration relationship between per capita consumption of red meat and the other variables. The results of the modelling work suggest that per capita consumption of red meat has an increasing trend. Additionally, a statistically significant short-term relationship was found between per capita consumption of red meat and the other variables. Given the relationship between consumption of red meat and level of economic development, the projections concerning red meat consumption are important from the viewpoint of the policies that will be formulated.

Application of Statistical Methods of Time-Series for Estimating and Forecasting the Wheat Series in Yemen (Production and Import)

Due to the importance of the wheat crop which represents 90% of the grain consumed, In this papers, we compared between the following statistical methods : Box and Jenkins model, exponential smoothing models (with trend and without seasonal) and Simple regression for estimating and forecasting to two time series of wheat(production and import). We reached to the following results: 1. Brown exponential smoothing model for modeling the imported wheat series. 2. ARIMA (1, 1, 1) model for modeling the product wheat series. For the wheat crop, the ratio of production to consumption is expected to reach 6.3% in 2015 and continues to decline even up to 5.4% in 2020. This means that the problem of food security well be worse in Yemen.

Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis

In this paper a few methods for anomaly detection in computer networks with the use of time series methods are proposed. The special interest was put on Brown's exponential smoothing, seasonal decomposition, naive forecasting and Exponential Moving Average method. The validation of the anomaly detection methods has been performed using experimental data sets and statistical analysis which has shown that proposed methods can efficiently detect unusual situations in network traffic. This means that time series methods can be successfully used to model and predict a traffic in computer networks as well as to detect some unusual or unrequired events in network traffic.

Trends and projections of vehicle crash related fatalities and injuries in Northwest Gondar, Ethiopia: A time series analysis

Background: Road traffic crashes are a huge public health and development problem in Ethiopia. Its current situation requires a high level political commitment, immediate decisions and actions in order to curb the growing problem. Materials and Methods: Data on fatalities, total and partial permanent injuries, and lost workday attributable to vehicle crashes were collected from North Gondar Traffic Offices from 1996 to 2011. Holt and Brown exponential smoothing techniques were used to model the number of fatalities and other injuries due to vehicle crashes. Results: There were 2300 vehicle crashes that occurred from 1996 to 2011 causing an estimated 968 fatalities, 1665 lost workday and 1185 permanent total and partial injuries, and 1,899,950.60$ losses. Only 7.6% of the vehicles had problems before the crashes occurred while 89.9% had no problems. The mean time of crashes occurred were 12.78 h with a standard deviation of 4.19 h. The highest daily, monthly, seasonal, and yearly crashes occurred were during Friday, January, winter and 2009, respectively. Future forecasts showed that by 2015, there could be 414 fatalities, 1123 lost workdays and 438 permanent total and partial injuries, and 955,249.12$ losses. Conclusion: The numbers of lives lost and disabilities due to vehicle crashes indicated an upward trend in the last decade showing future burden in terms of societal and economic costs threatening the lives of many individuals. Surveillance systems that could enable to monitor patterns of vehicle crashes with preventive strategies must be established.

Cost-benefit analysis of the introduction of mass vaccination against hepatitis B in Italy.

The aim of the study was to evaluate the costs and benefits of introducing mass vaccination against hepatitis B in Italy, given the announcement of compulsory vaccination of all newborn babies from 1991. Benefits were calculated by summarizing the clinical course of hepatitis B in Italy and projecting its incidence rates to the next 30 years using Brown's exponential smoothing technique. Incidence rates were then applied to the survivors of a cohort of newborns in 1992, and the total number of cases avoidable through vaccination was derived. Direct and indirect marginal costs were estimated for these cases. The marginal costs of the vaccination campaign were estimated. Cost and benefits were compared for 99 years following the introduction of the vaccination, using an 8 per cent discount rate and a sensitivity analysis. Despite difficulties in data gathering, susceptibility to discounting and estimating costs, results show an unfavourable cost-benefit ratio which is influenced by a declining incidence of the disease. Vaccination of high-risk groups is probably more efficient.