Exponentially Weighted Moving Average Method Research Articles

Comparison of Weekly Training Load and Acute: Chronic Workload Ratio Methods to Estimate Change in Training Load in Running.

Before examining the impact of training load on injury risk in runners, it is important to gain insight into the differences between methods that are used to measure change in training load. To investigate differences between 4 methods when calculating change in training load: (1) weekly training load; (2) acute : chronic workload ratio (ACWR), coupled rolling average (RA); (3) ACWR, uncoupled RA; (4) ACWR, exponentially weighted moving average (EWMA). Descriptive epidemiology study. This study is part of a randomized controlled trial on running injury prevention among recreational runners. Runners received a baseline questionnaire and a request to share global positioning system training data. Runners who registered for running events (distances 10-42.195 km) in the Netherlands. The primary outcome measure was the predefined significant increase in training load (weekly training loads ≥ 30% progression and ACWRs ≥ 1.5), based on training distance. Proportional Venn diagrams visualized the differences between the methods. A total of 430 participants (73.3% men; mean age = 44.3 ± 12.2 years) shared their global positioning system training data for a total of 22 839 training sessions. For the weekly training load, coupled RA, uncoupled RA, and EWMA method, respectively, 33.4% (95% CI = 32.8, 34.0), 16.2% (95% CI = 15.7, 16.6), 25.8% (95% CI = 25.3, 26.4), and 18.9% (95% CI = 18.4, 19.4) of the training sessions were classified as significant increases in training load. Of the training sessions with significant increases in training load, 43.0% from the weekly training load method were different than the coupled RA and EWMA methods. Training sessions with significant increases in training load based on the coupled RA method showed 100% overlap with the uncoupled RA and EWMA methods. The difference in the change in training load measured by weekly training load and ACWR methods was high. To validate an appropriate measure of change in training load in runners, future research on the association between training loads and running-related injury risk is needed.

Method EWMA (Exponentially Weighted Moving Average) as a Filter to Fine and Remove Noise on Time Series Data

The Moving Average method is the most common filter in Digital Signal Processing, the Moving Average method is optimal for reducing unstable value fluctuations due to interference by maintaining accurate readings. Therefore it is often used for signal coding in the context of time (Smith, 1999). The kind of moving average method is the Exponentially Weighted Moving Average (EWMA) which is often applied to a time sequence of random variables, by calculating the weighted average of the sequence by applying a weight that decreases geometrically with the length of observation (Perry, 2011). this method can be applied to tools/instruments that use data continuously in a certain time so that it will require a filter so that the fluctuations in the data are not extreme which will result in changes in the output response being softer so as not to damage the device on the output. This study uses the EWMA method to eliminate noise in the processing of sensor readings. The results of the study, the system is able to monitor realtime ph and temperature data. In fish farming, measuring and monitoring water temperature and ph is important, if it is not regularly measured and monitored it can cause various problems such as pH and temperature that are not optimal for fish growth and health. The research method to be carried out is R&D, EWMA will be applied to a multi-sensor circuit then processed by a data logger device to then be displayed on serial data on its output.

Assessment of patient based real-time quality control on comparative assays for common clinical analytes.

BackgroundIt is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient‐based real‐time quality controls (PBRTQCs) on comparative assays to monitor the consistency among clinical laboratories.MethodsThe present study included 11 commonly tested analytes that were detected using three analyzers. PBRTQC procedures were set up with exponentially weighted moving average (EWMA) algorithms and evaluated using the AI‐MA artificial intelligence platform. Comparative assays were carried out on serum samples, and patient data were collected. Patients were divided into total patient (TP), inpatient (IP), and outpatient (OP) groups.ResultsOptimal PBRTQC protocols were evaluated and selected with appropriate truncation limits and smoothing factors. Generally, similar comparative assay performance was achieved using both the EWMA and median methods. Good consistency between the results from patients' data and serum samples was obtained, and unacceptable bias was detected for alkaline phosphatase (ALP) and gamma‐glutamyl transferase (GGT) when using analyzer C. Categorizing patients' data and applying specific groups for comparative assays could significantly improve the performance of PBRTQCs. When monitoring the inter‐ and intraanalyzer stability on a daily basis, EWMA was superior in detecting very small quality‐related changes with lower false‐positive alarms.ConclusionsWe found that PBRTQCs have the potential to efficiently assess multianalyzer comparability. Laboratories should be aware of population variations concerning both analytes and analyzers to build more suitable PBRTQC protocols.

Review on Characteristics and Applicability of Digital Filter Baseflow Separation Methods

Baseflow separation method based on the digital filter is a technique for separating the baseflow from the total streamflow, and appropriate estimation of the filter parameter is required for analysis. In this study, we reviewed the analysis characteristics of the four baseflow separation methods (Lyne-Hollick, Chapman, Eckhardt, and EWMA (exponentially weighted moving average)) based on the digital filter according to the change in the filter parameter and evaluated their applicability. Each digital filter method was applied to Daeri and Cheonsong stage stations, which have measurement records for five consecutive years for tributaries located of the Nakdonggang River in the Republic of Korea. The Chapman method had an error in the baseflow separation during the dry season. The EWMA method was not appropriately analyzed within the parameter range used in previous studies. The Lyne-Hollick and Eckhardt methods provided appropriate results within the exploring range of the filter parameter. However, in the Eckhardt method, we had to be careful in determining the filter parameter because the variation in analysis results was relatively large according to the parameter change.

Baseflow Separation Using the Digital Filter Method: Review and Sensitivity Analysis

The baseflow separation method based on a digital filter is a simple method for separating the baseflow from streamflow. Appropriate estimation of filter parameters is required to use the digital filter method for analysis. We carried out sensitivity analysis on four digital filter methods: Lyne–Hollick (LH), Chapman, Chapman and Maxwell (CM), and exponentially weighted moving average (EWMA). Furthermore, appropriate filter parameters were suggested for each method in this study. By applying them to 25 stage stations in the Nakdong River in the Republic of Korea, the four methods were evaluated. The results of the evaluation showed that the Chapman and CM methods had problems separating the baseflow during the dry seasons. The EWMA and LH methods were able to achieve reliable baseflow separation of the outcomes by selecting appropriate the filter parameters. Thus, the EWMA and LH methods can be used easily and reasonably among the digital filter methods that have one filter parameter.

Comparing statistical process control charts for fault detection in wastewater treatment.

Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as set-point tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.

GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)

Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

AbstractThe strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance

BackgroundSystematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.MethodsFollowing dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.ResultsEWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.ConclusionsSPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.

A dinâmica da taxa de câmbio em países emergentes: uma perspectiva Pós-Keynesiana dos casos do Brasil e do México entre 2000 e 2017

Na perspectiva pós-keynesiana, a taxa de câmbio é considerada um tipo de ativo financeiro sujeito às decisões de alocação de portfólio dos agentes que atuam em um sistema monetário e financeiro internacional hierárquico e assimétrico. Baseado nesta perspectiva, este artigo realiza um estudo de caso comparativo das taxas de câmbio do Brasil e do México nos anos 2000, destacando a influência de fatores institucionais. O estudo cobre seis fases do ciclo financeiro internacional e utiliza o Método de Média Móvel Exponencial e o coeficiente de Pearson para analisar a relação entre as taxas de câmbio e os determinantes da equação keynesiana. Os resultados mostram que, embora as taxas de câmbio estudadas tenham um co-movimento semelhante, existem diferenças importantes em relação à sua volatilidade e ao impacto desses determinantes.

An Efficient Routing Scheme for Intrabody Nanonetworks Using Artificial Bee Colony Algorithm

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables

Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods.

Midpoint-radii principal component analysis -based EWMA and application to air quality monitoring network

Monitoring air quality is crucial for the safety of humans and the environment. Moreover, real world data collected from air quality network is often affected by different types of errors as measurement noise and variability of pollutant concentrations. The uncertainty in the data, which is strictly connected to the above errors, may be treated by considering interval-valued data analysis. In practical cases of measured data, the true value cannot be measured and the collected data on a process are only approximations given by sensors, and are thus imprecise. This is due mainly to the uncertainties induced by measurement errors or determined by specific experimental conditions. Thus, the main aim of this paper is to develop an enhanced monitoring of air quality network by taking into account the uncertainties on the data. To do that, we develop a new monitoring technique that merges the advantages of Midpoint-radii PCA (MRPCA) method with exponentially weighted moving average (EWMA) chart, in order to enhance sensor fault detection technique of air quality monitoring process. MRPCA is the most popular interval multivariate statistical method, able to tackle the issue of uncertainties on the models and one way to improve the fault detection abilities. On the other hand, the EWMA statistic allows an exponential weighted average to successive observations and able to detect small and moderate faults. The developed MRPCA-based EWMA method relies on using MRPCA as a modeling framework for fault detection and EWMA as a detection chart. The proposed MRPCA-based EWMA scheme is illustrated using a simulation example and applied for sensor fault detection of an air quality monitoring network. The monitoring performances of the developed technique are compared to the classical monitoring techniques. MRPCA model performances are compared with the interval PCA models: complete-information principal component analysis (CIPCA) and Centers PCA (CPCA). The MRPCA-based EWMA monitoring performances are compared to MRPCA-based Shewhart, generalized likelihood ratio test (GLRT) and squared prediction error (SPE) techniques.

Monitoring breeding herd production data to detect PRRSV outbreaks

Porcine reproductive and respiratory syndrome virus (PRRSv) causes substantial economic impact due to significant losses in productivity. Thus, measuring changes in farm productivity before and after PRRS infection enables quantifying the production and economic impact of outbreaks. This study assessed the application of exponentially weighted moving average (EWMA), a statistical process control method, on selected production data (number of abortions, pre-weaning mortality rate and prenatal losses) to supplement PRRS surveillance programs by detecting significant deviations on productivity in a production system with 55,000 sows in 14 breed-to-wean herds in Minnesota, U.S.A. Weekly data from diagnostic monitoring program (available through the Morrison’s Swine Health Monitoring Project) implemented on the same herds was used as reference for PRRS status. The time-to-detect, percentage of early detection of PRRSv-associated productivity deviations, and relative sensitivity and specificity of the production data monitoring system were determined relative to the MSHMP. The time-to-detect deviations on productivity associated with PRRS outbreaks using the EWMA method was −4 to −1 weeks (interquartile range) for the number of abortions, 0–0 for preweaning mortality and −1 to 3 weeks for prenatal losses compared to the date it was reported in the MSHMP database. Overall, the models had high relative sensitivity (range 85.7–100%) and specificity (range 98.5%–99.6%) when comparing to the changes in PRRS status reported in the MSHMP database. In summary, the use of systematic data monitoring showed a high concordance compared to the MSHMP-reported outbreaks indicating that on-farm staff and veterinary oversight were efficient to detect PRRSv, but can be more efficient if they were monitoring closely the frequency of abortions. The systematic monitoring of production indicators using EWMA offers opportunity to standardize and semi-automate the detection of deviations on productivity associated with PRRS infection, offering opportunity to early detect outbreaks and/or to quantify the production losses attributed to PRRS infection.

AN EXPONENTIALLY WEIGHTED MOVING AVERAGE METHOD WITH DESIGNED INPUT DATA ASSIGNMENTS FOR FORECASTING LIME PRICES IN THAILAND

In this paper, the Exponentially Weighted Moving Average (EWMA) method with designed input data assignments (i.e. the proposed method) is presented to forecast lime prices in Thailand during January 2016 to December 2016. The lime prices from January 2011 to December 2015 as the input data are gathered from the website’s database of Simummuang market, which is one of the big markets in Thailand. The novelty of our paper is that although the performance of the EWMA method significantly decreases when applying to forecast data which show trend and seasonality behaviors and the EWMA method is used for short-range forecasting (i.e. usually one month into the future), the proposed method can properly handle such mentioned problems. For this purpose, to forecast lime prices, five different input data are intently defined before assigned to the EWMA method: a) the monthly data of the year 2015 (i.e. the recent year data), b) the average monthly data of the year 2011 to 2015, c) the median of the monthly data of the year 2011 to 2015, d) the monthly data of the year 2011 to 2015 after applying the linear weighting factor, where the higher weight value is applied to the recent data, and e) the average monthly data of the year 2011 to 2015 after applying the exponential weighting factor, where the higher weight is also applied to the recent data. These designed input data are used as agents of the raw data. Our study reveals that using the input data b) with the EWMA method to forecast lime prices during January 2016 to September 2016 gives the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE). Forecasted lime prices of October 2016 to December 2016 are also provided. Additionally, we demonstrate that the proposed method works well compared with the Double Exponentially Weighted Moving Average (DEWMA), the Multiplicative Holt-Winters (MHW), and the Additive Holt-Winters (AHW) methods, which are suitably used for forecasting data with the trend and the seasonality.

Sampling Schemes by Variables Inspection for the First-Order Autoregressive Model between Linear Profiles

We present four new sampling schemes by variables inspection to deal with the first-order autoregressive model between linear profiles. The first plan is based on exponentially weighted moving average (EWMA) and the rest of three plans are using the resubmitted sampling, repetitive group sampling (RGS), and multiple dependent state (MDS) sampling schemes. The nonlinear optimization problem is developed to find the number of profiles and the corresponding acceptance criteria, such that the producer’s and consumer’s risk are satisfied simultaneously. The efficiency of the proposed plans is compared with the conventional single sampling plan in terms of average sample number and the probability of acceptance. The result implies that all of the proposed sampling plans are superior to the single acceptance sampling plan by variables. In addition, the EWMA method appeared to be better than the others. The applications of proposed plans are shown with the help of industrial examples taken from calibration of an optical imaging system, and tire cornering stiffness test.

A refrigerant charge fault detection method for variable refrigerant flow (VRF) air-conditioning systems

Degradation occurs in a VRF system after years of operation due to refrigerant leakage, mechanical failure or improper maintenance. VRF systems require approaches to detect faults and sustain its normal operation. This paper proposes a creative statistical method to detect the refrigerant charge faults in VRF systems, which is based on principal component analysis (PCA) and exponentially-weighted moving average (EWMA) control charts. The EWMA model is built with the residual vector of the PCA model. Data of the experimental VRF system is used to validate the advantages of the PCA–EWMA method. Results show that the combined use of PCA and EWMA methods can achieve better fault detection efficiency than PCA based T2-statistic and Q-statistic methods at low fault severity levels. The robustness of the PCA–EWMA method in online fault detection is verified using the data from different type of VRF systems.

PBS machine interlocks using EWMA

Delivery of pencil beam scanning (PBS) requires the on-line measurement of several beam parameters. If the measurement is outside of specified tolerances and a binary threshold algorithm is used, the beam will be paused. Given instrumentation and statistical noise such a system can lead to many pauses which could increase the treatment time. Statistical quality control methods are typically used on manufacturing lines to monitor a process and give early detection of a gradual problem and stop the process if a deviation is statistically significant. These methods can be used to develop a more intuitive algorithm for (PBS) delivery systems that is robust and safe and leads to decreased treatment times. The Exponentially Weighted Moving Average (EWMA) control scheme monitors deviations in beam properties which are averaged over a specified number of measurements with greater weight applied to the more recent ones. Simulation of an EWMA-style algorithm safely detected shifts in random and systematic delivery errors without false alarms. Binary and EWMA methods can be combined for improved reliability without sacrificing patient safety. In the EWMA method, the mean of a beam property can be related to systematic uncertainties and the standard deviation can be related to random uncertainties. This method allows one to have separate interlock levels for each type of uncertainty and to detect systematic trends.

PLS-based EWMA fault detection strategy for process monitoring

Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T2 and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.

A comparison of exponentially weighted moving average-based methods for monitoring increases in incidence rate with varying population size

Estimation of incidence rate and quick detection of its increases are important tasks in public health surveillance. In addition to being an efficient tool for online parameter estimation, the Exponentially Weighted Moving Average (EWMA) method has been widely used as an effective monitoring tool in statistical process control. Motivated by its successful applications, several EWMA-type methods are discussed for monitoring and estimating the incidence rate of adverse events in health care applications. The comparison results show that the conventional EWMA chart has a superior performance in detecting small shifts that occur at the start-up but very poor performance when shifts occur at a later time point. Instead, the adaptive EWMA method that is capable of dynamically updating its smoothing parameter can provide an overall good detection performance when shifts occur at both the first time point and a later time point. This result is validated using male thyroid cancer data in New Mexico.