Smoothing Method Research Articles

An Evaluation of Projection Methods for Detailed Small Area Projections: An Application and Validation to King County, Washington.

Population projections are used by a number of local agencies to better prepare for the future resource needs of counties, ensuring that educational, health, housing, and economic demands of individuals are met. Meeting the specific needs of a county's population, such as what resources to provide, where to target resources, and ensure an equitable distribution of those resources, requires population projections which are both demographically detailed, such as by age, race, and ethnicity, and geographically precise, such as at the census tract level. Despite this need, an evaluation of which methods are best suited to produce population projections at this level are lacking. In this study, we evaluate the accuracy of several cohort-based methods for small area population projections by race and ethnicity. We apply these methods to population projections of King County, Washington and assess the validity of projections using past population estimates. We find a clear pattern that demonstrates while simplified methods perform well in near term forecasts, methods which employ smoothing strategies perform better in long-term forecasting scenarios. Furthermore, we demonstrate that model's incorporating multiple stages of smoothing can provide detailed insights into the projected population size of King county and the places and groups which will most contribute to this growth. Detailed projections, such as those provided by multi-stage smoothing methods, enable city planners and policy makers a detailed view of the future structure of their county's population and provide for them a resource to better meet the needs of future populations.

Peramalan Jumlah Produksi Buah Nanas di Provinsi Riau Menggunakan Metode Single Exponential Smoothing

Forecasting is an attempt to project future events using relevant information from the past. Using such data, forecasting serves as an effective means of planning activities and reducing the likelihood of errors in predicting the course of future events. One method of prediction is the Single Exponential Smoothing method. The research is aimed at predicting the amount of pineapple production in Riau Province by 2023. The study utilizes information on the number of pines production in the Riau province as many as 10 datasets covering the period from 2013 to 2022. This data is obtained from the official website of BPS Province Riau. In this study, the researchers used quantitative methods. Based on a solver analysis in Ms.Excel, the results showed that the forecast accuracy rate reached 71.64% with the smallest error presentation of about 28.36% which categorized these forecasts quite well. Using this method, the forecast quantity of pineapple production in Riau Province for 2023 is 262,637 tons.

Supply Control Of Oil Filter Spareparts Using EOQ And Min-Max Methods Based On Sales Forecast (Case Study: PT Astra International Tbk Soekarno Hatta)

Planning and controlling product inventory is very important for a company to ensure demand is met and maximize profits from sales effectively and efficiently. In addition, inventory planning and control are carried out to prevent excess or shortage of inventory. This study aims to predict sales and calculate inventory control to overcome excess stock and increased inventory costs on oil filter spare parts. The forecasting method used is Holt's Double Exponential Smoothing method to determine the number of sales of the four types of oil filter spare parts in the future and overcome the problem of oil filter spare parts inventory using the EOQ and Min-Max methods. Based on the research results, the resulting forecasting is accurate and the EOQ method results in a lower total inventory cost than the Min-Max method.

METODE TRIPLE EXPONENTIAL SMOOTHING HOLT-WINTER’S MULTIPLICATIVE DAN DEKOMPOSISI KLASIK MULTIPLIKATIF UNTUK PERAMALAN RATA-RATA KENAIKAN KONSENTRASI KARBON DIOKSIDA (CO2) GLOBAL

Global warming occurs due the high concentration of Greenhouse Gases (GHG) in the atmosphere, which is called the greenhouse effect. The highest greenhouse gas that causes global warming that is being piled up in the atmosphere due human activity is carbon dioxide. Data on the average increase in global carbon dioxide (C02) concentrations are assumed contain elements of trend and seasonality. Holt-Winter's Multiplicative Triple Exponential Smoothing Method and Multiplicative Classical Decomposition the best choices in predicting data that contains trend and seasonality elements. Forecasting data on the global average increase CO2 has the objective of predicting data for the next 12 periods. The data used is data on the global average increase for the period January 2013 to December 2022. The prediction error measure used is MAPE (Mean Absolute Percentage Error). The results of the analysis on the Triple Exponential Smoothing Holt-Winter's Multiplicative method obtained a MAPE value of 0.09395%, indicating very good prediction category, while the results of the analysis of the Multiplicative Classical Decomposition method had a MAPE value of 0.07021%, which means that it has very good category in do forecasting. Based on the MAPE value obtained, the best method is the Multiplicative Classical Decomposition method.

Surface diffusion on a palladium-based metallic glass

The surface diffusion kinetics on a Pd77.5Cu6Si16.5 metallic glass is measured using a scratch smoothing method in the range of 107–57 K below the glass transition temperature. Within this temperature range, the surface diffusion coefficients are determined to vary between (8.66 ± 0.80) × 10−19 and (5.90 ± 0.60) × 10−18 m2 s−1. The corresponding activation energy is 0.93 ± 0.18 eV, which is about half the value for bulk diffusion. These measurements also corroborate the correlation between enhanced surface diffusion and liquid fragility in glasses.

Dynamic evaluation of ash deposition behavior and parametric study using an inverse distance weighted interpolation dynamic mesh method

Ash deposition is a critical issue concerning the energy utilization and safety operation of heat exchangers. Efficient dynamic evaluation of deposition morphology and reasonable structural parameters contribute to the deposition prediction and the improvement of deposition resistance. In this work, a transient deposition model is established to investigate the dynamic deposition behavior and comprehensive performance. To realize the real-time update of deposition layer morphology, a simple and easy-to-implement dynamic mesh smoothing method based on inverse distance weighted (IDW) interpolation is proposed and then validated by experiments. The effect of the deposition layer dynamic evolution on the particle impact characteristics and the thermal-hydraulic performance of tube bundles is highlighted. In addition, parametric studies are performed on the tube arrangement to obtain the optimal comprehensive performance design. The results show that the angles and velocities of particles impacting the first tube and downstream tubes present a triangular distribution and a butterfly-shaped spatial distribution, respectively. Due to the guidance of the deposition layer on the trajectory of gas and ash particles, the particle impact region on the windward side is more concentrated when using the dynamic mesh model, and the entrainment of small-sized particles is intensified. Besides, the rise in longitudinal spacing significantly exacerbates the deposition and heat transfer deterioration. According to the comprehensive evaluation, the case at SL /D = 2.5 exhibits a higher deposition resistance and heat transfer performance. These results can provide more realistic predictions and indications in practical thermos-equipment design.

Forecasting Raw Material Demand for Battery Breaker Production Process

This study aims to identify the most effective forecasting method for predicting raw material demand in the tin smelting industry, addressing the challenge of uncertainty in material arrival and inaccurate demand forecasting. Three methods, namely moving averages with n = 3 and n = 5, and exponential smoothing, were evaluated using historical data. Results indicate that exponential smoothing with α = 0.2 outperformed the other methods, yielding the smallest error rate with a Mean Absolute Percentage Error (MAPE) of 23%, Mean Absolute Deviation (MAD) of 411, and Mean Squared Error (MSE) of 293303. The implication of these findings underscores the importance of employing appropriate forecasting techniques to optimize inventory management and mitigate shortages in critical industries reliant on volatile raw material supplies. Highlights : Accurate demand forecasting is crucial for companies engaged in smelting to prevent shortages and inventory increases. Three methods were used to determine the most appropriate forecasting method for raw material demand based on historical data: moving average with n = 3 and n = 5, and exponential smoothing with α = 0.2. The Exponential Smoothing Method with α = 0.2 had the smallest error rate, with a MAPE value of 23%, MAD of 411, and MSE of 293303, and can be used to optimize demand forecasting for the next period. Keywords: demand forecasting, smelting, raw materials, historical data, moving average, exponential smoothing.

Trends by age and sex and projections of disease prevalence from studying hypertensive heart disease in China over the past three decades.

Hypertensive heart disease (HHD) is a common cause of cardiovascular disease and mortality worldwide, and its burden is increasing with aging populations. This study aimed to estimate the prevalence and mortality rates of HHD in mainland China and Taiwan Province using data from the Global Burden of Disease Study 2019 (GBD 2019), and forecast the development trend of HHD from 2020 to 2024. We obtained data on number of cases, deaths, crude prevalence rate, crude death rate, age-standardized prevalence rate (ASPR), and age-standardized death rate (ASDR) for mainland China and Taiwan Province from 1990 to 2019 from the GBD 2019. Joinpoint software was used to estimate average annual percentage change (AAPC) with 95% confidence intervals, and the number of HHD cases in China from 2022 to 2024 was predicted by the exponential smoothing method. Between 1990 and 2019, HHD cases and deaths increased in mainland China, but the ASPR and ASDR decreased by 5.96% and 48.72%, respectively. In Taiwan Province, ASPR and ASDR decreased by 7.66% and 52.14%, respectively. The number of HHD cases and death rates varied by region, age, and sex, with a higher number of cases in mainland China than in Taiwan Province. By 2024, the number of HHD cases in mainland China was projected to be over 9.6 million cases, and in Taiwan Province, it was projected to surpass 120,000 cases. The differences in HHD cases between mainland China and Taiwan Province in terms of age and sex indicated the need for effective prevention and control measures, especially targeting the elderly population. These findings can inform policymakers and health professionals in the development of targeted prevention and treatment strategies and resource allocation for HHD in China.

An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.

This article introduces bayesian spatial smoothing models for disease mapping-a specific application of small area estimation where the full universe of data is known-to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space-time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers.

The impact of TV advertising on website traffic

AbstractWe propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the nonlinear dependence of the estimated increase in website visits on characteristics of the ads using the random forest method. The proposed methodology is applied to a dataset containing minute‐by‐minute organic website visits and detailed characteristics of TV ads for an e‐commerce company in 2019. The results show that people are indeed willing to switch between screens and multitask. Moreover, the time of the day, the TV channel, and the advertising motive play a great role in the impact of the ads.

Forecasting Rice Prices with Holt-Winter Exponential Smoothing Model

Rice, as a staple food, plays a crucial role in global food security. Accurate forecasting of rice prices is essential for policymakers, farmers, and consumers alike. This article explores the application of the Holt-Winter exponential smoothing model to predict rice prices. Holt-Winter method is chosen for its ability to capture both trend and seasonality in time series data, which are prominent features in agricultural commodity prices such as rice. The study analyzes historical price data, identifies trends, seasonality, and incorporates smoothing parameters in additive and multiplicative methods. Results indicate that additive method of Holt-Winter exponential smoothing provides a better performance. This research contributes valuable insights to the field of agricultural economics and informs strategies for managing food supply chains and market stability.

Smoothed quantile residual life regression analysis with application to the Korea HIV/AIDS cohort study

BackgroundThe residual life of a patient with human immunodeficiency virus (HIV) is of major interest to patients and their physicians. While existing analyses of HIV patient survival focus mostly on data collected at baseline, residual life analysis allows for dynamic analysis based on additional data collected over a period of time. As survival times typically exhibit a right-skewed distribution, the median provides a more useful summary of the underlying distribution than the mean. In this paper, we propose an efficient inference procedure that fits a semiparametric quantile regression model assessing the effect of longitudinal biomarkers on the residual life of HIV patients until the development of dyslipidemia, a disease becoming more prevalent among those with HIV.MethodsFor estimation of model parameters, we propose an induced smoothing method that smooths nonsmooth estimating functions based on check functions. For variance estimation, we propose an efficient resampling-based estimator. The proposed estimators are theoretically justified. Simulation studies are used to evaluate their finite sample performances, including their prediction accuracy. We analyze the Korea HIV/AIDS cohort study data to examine the effects of CD4 (cluster of differentiation 4) cell count on the residual life of HIV patients to the onset of dyslipidemia.ResultsThe proposed estimator is shown to be consistent and normally distributed asymptotically. Under various simulation settings, our estimates are approximately unbiased. Their variances estimates are close to the empirical variances and their computational efficiency is superior to that of the nonsmooth counterparts. Two measures of prediction performance indicate that our method adequately reflects the dynamic character of longitudinal biomarkers and residual life. The analysis of the Korea HIV/AIDS cohort study data shows that CD4 cell count is positively associated with residual life to the onset of dyslipidemia but the effect is not statistically significant.ConclusionsOur method enables direct prediction of residual lifetimes with a dynamic feature that accommodates data accumulated at different times. Our estimator significantly improves computational efficiency in variance estimation compared to the existing nonsmooth estimator. Analysis of the HIV/AIDS cohort study data reveals dynamic effects of CD4 cell count on the residual life to the onset of dyslipidemia.

Forescating the Amount of Corn Production in North Sumatra Based on 2017 – 2021 Data Using The Single and Double Exponential Smoothing Method (Case Study of Central Bureau of Statistics of North Sumatra )

North Sumatra Province has high potential in the management and marketing of corn (Zae Mays) crops. Cultivation of corn plants is a superior product where the largest income is obtained from the sale of commodities and their processing so that they can assist the government in improving the economy. This study aims to determine the yield of corn in North Sumatra Province in the coming year. Data collection for this study uses secondary data, namely primary data obtained from other parties which are generally made in the form of tables and diagrams. Data obtained from the Central Bureau of Statistics of North Sumatra for corn production in 2017 was 1,741,257 tons, corn production in 2018 was 1,710,784 tons, corn production in 2019 was 1,960,424 tons, corn production in 2020 was 1,965. 444 tons, corn production in 2021 is 1,724,398 tons. To find out the increase in corn production, the consideration and comparison of the forecasting methods needed to minimize forecast errors that aim to approach reality are the single and double exponential smoothing methods with one parameter from Brown.

Co-Operatively Increasing Smoothing and Mapping Based on Switching Function

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system’s robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation.

Exploring the Impact of Different Milling Parameters of Fe/SiO2 Composites on Their Structural and Magnetic Properties.

This research focuses on the production process of soft magnetic composites in the form of 3D bulk compacts made from insulated powder particles using different milling parameters, aiming to enhance their magnetic properties and to study an innovative method of the powder surface "smoothing" technique. A structure analysis using scanning electron microscopy (SEM), EDS, and optical microscopy is also included. We found out that the samples made by the innovative method have lower density values. This can be caused by a more consistent SiO2 insulation layer on highly pure iron powder particles. A correlation between the mechanical smoothing method and better insulation of powder particles can help to provide eco-friendlier solutions for the preparation of soft magnetic composites, such as less usage of reagents and more consistent coverage of powder particles with lower final insulation thickness. The magnetic properties of these compacts are evaluated by coercive field, permeability, and loss measurements. The particle-level smoothing technique in some cases can reduce the value of coercivity up to 20%. For some samples, the ball-to-powder ratio has a bigger impact on magnetic properties than surface treatment, which can be caused by an increased amount of insulation in the SMC compacts.

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

The Forecasting of Transportation of People by Rail in Poland for 2024 in Terms of Economic Security in the Republic of Poland

The multidimensional comparative analyzes of transportation of people by rail were used in the article. The time series was analyzed and evaluated in order to detect the following phenomena: trend, seasonality and random factor. The initial time series was divided into parts in order to remove data that, due to the impact of a random phenomenon such as the COVID-19 pandemic, lost trends visible in the past (from January 2012 to December 2019). The Winters’ exponential smoothing method was used for the forecasting. The obtained forecast for 2024 is 390 380 000 passengers transported by rail in Poland. The mean absolute forecast error is 2,18.

Анализ и прогнозирование трафика современных телекоммуникационных систем на основе методов искусственного интеллекта

In recent years, artificial intelligence technologies have demonstrated significant success in solving the problem of traffic analysis and forecasting in various telecommunication systems. Forecasting allows the telecom operator to know about the future behavior of the network, take timely necessary measures to improve the quality of customer service, and decide on the need to install or upgrade equipment. Using data collected from IoT mobile devices as an example, this article provides an overview and analysis of various time series forecasting models describing the traffic behavior of telecommunication systems. Forecasting models such as the exponential smoothing method, linear regression, the autoregressive integrated moving average (ARIMA) method, the support vector machine regression method, the N-BEATS method, which uses fully connected layers of a neural network for forecasting a one-dimensional time series, are discussed; the features of some of them are briefly outlined. For a specific data array, data preparation operations are described: removing unused columns, replacing missing data on transaction durations with their median values, and describing the main statistical characteristics of the data array. A preliminary data analysis is presented, which consists of using smoothing methods: moving average and exponential smoothing. The process of training models and a comparative analysis of the quality of their training are described. For this data set, it was concluded that for the UDP protocol the ARIMA model has the best learning quality, for the TCP protocol - linear regression and the Theta model, for the HTTPS protocol – linear regression, ARIMA and N-BEATS.

Exploring the air impacts on the state development of pipe flow using the smooth particle hydrodynamic method

Abstract It is widely recognized that the water flow in pipes can be affected by trapped air pockets. However, the underlying air–water interactions of free flow with non-jacking downstream of the pipe are rarely investigated. There are no studies at this time that clearly elucidate the differences in pipe flow between the vacuum and the presence of air in the free flow case. To this end, the smoothed particle hydrodynamics (SPH) method is applied to study the air–water hydraulic dynamics in the pipe under free flow. We perform SPH simulations in several different scenarios and find that (i) the SPH method is competent to simulate both single- and two-phase flows in the pipe due to its outstanding advantages in capturing complex interfaces; (ii) only when the upstream water level is higher than the top of the pipe inlet, and the water level in the pipe rises to the top of the pipe due to air resistance, can a full pipe flow be formed; and (iii) the presence of air can cause the water to form a full pipe flow, causing a siphon-like effect that promotes pipe drainage. These results provide insights into the underlying complex air–water hydraulic properties in pipe flows.

A general parametric finite element modeling and stress response analysis methods for pipeline system using the nonconforming solid element

In the dynamic topology optimization design of pipeline system, parametric finite element modeling (PFEM) is needed to improve the model reconstruction efficiency. Taking a typical spatial single pipeline as the research object, this paper innovatively presents a PFEM method of pipeline system based on the newly constructed nonconforming solid (Solid-NC) element. The stiffness and mass matrices of the spatial 8-node Solid-NC element with second-order boundary accuracy are obtained by introducing the node-free displacement items and statics condensation method. The PFEM method of the pipeline body is proposed by selecting the straight-line segment lengths as the pipeline shape control parameters. The PFEM method of the pipeline is described in detail, including the node coordinate solutions of straight-line and curved arc segments based on the direction vector method and vector decomposition method, respectively, and the simulation of the clamp mechanical properties considering the actual pre-tightening state of the clamp. Furthermore, the modal analysis is carried out, the node stress response solution is solved based on the stress smoothing method, and the effectiveness of the PFEM method is verified through experiment. Finally, the mesh quality and model reconstruction efficiency of the PFEM method is analyzed, the results show that the developed PFEM method has higher mesh quality and fast model reconstruction speed, and the model reconstruction time is shortened by 544.5 times at most compared with ANSYS software.