1,721,358 research outputs found
An Adaptive Strategy for Wind Speed Forecasting Under Functional Data Horizon: A Way Toward Enhancing Clean Energy
Get PDFAn important issue in competitive energy markets is the accurate and efficient wind speed forecasting for wind power production. However, wind speed forecasting models developed for one location usually do not match the other site for various reasons like changes in terrain, different wind speed patterns, and atmospheric factors such as temperature, pressure, humidity, etc. Thus, introducing a flexible model that captures all the features is a challenging task. This paper proposes a functional data analysis (FDA) approach to forecast the site variant wind daily profiles with higher accuracy. Unlike the traditional methods, the FDA is more attractive as it forecasts a complete daily profile, and thus, forecasts can be obtained in the ultra-short period. To this end, the wind speed data is first filtered for extreme values. The filtered series is then divided into deterministic (Component-I) and stochastic (Component-II) components. Component-I is modeled and forecasted based on the generalized additive modeling technique. On the other hand, Component-II is modeled and forecasted using functional models such as functional autoregressive (FAR) and FAR with explanatory variables (FARX). For comparison purposes, forecasts from the traditional univariate autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), SARIMA with exogenous information (SARIMAX), and neural network autoregressive (NNAR) models are also obtained. For empirical analysis, the wind speed data are obtained from the NASA power project for the site Canada located in Durham, England, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting performance of different models is assessed through different accuracy measures, namely mean error, root mean squared error, mean absolute error, and mean absolute standard error. The results indicate that the functional models outperform the classical ARIMA, SARIMA, SARIMAX, and a deep learning model, NNAR. Within the functional models, the forecasting ability of the FARX is superior to FAR
An efficient MEWMA chart for Gumbel's bivariate Pareto distribution
Get PDFControl charts play a vital role in process monitoring to ensure the product's desired standards. Due to rapid improvements in data collection methods, multivariate charts are preferred over univariate charts. This paper proposes a bivariate exponentially weighted moving average chart for the simultaneous monitoring of the mean vector of Gumbel's bivariate Pareto type II (also known as the Lomax distribution) time-between-events data. The performance of the proposed chart is assessed through average run length, median run length, and the standard deviation of the run length criteria. To show the implementation of the chart in the real world, illustrative examples are also presented
Exponentially weighted moving average chart using zero-inflated negative binomial distribution
No full textZero-inflated models are frequently used to deal with data having many zeros. A commonly used model for over-dispersed data containing zeros is known as the zero-inflated Poisson model. However, to account for the heterogeneity of counts that leads to excess variance besides inflation of zeros in the data using a more flexible model than the zero-inflated Poisson model, a zero-inflated negative binomial (ZINB) is suggested. In the present study, Shewhart and exponentially weighted moving average (EWMA) control charts are suggested to monitor the ZINB data. The charts are compared using the average run length and standard deviation of run length by using extensive Monte Carlo simulations. Besides a comprehensive simulation study assuming different settings of parameters of ZINB, a real data set is used to show the practicality of the proposed charts. The results indicate that the EWMA chart is better than the Shewhart chart
Parameters Estimation of the Exponentiated Chen Distribution Based on Upper Record Values
No full textThis article discusses the Bayesian and frequentist inferences for the exponentiated Chen distribution assuming upper record values. Due to unavailability of the compact form of marginal posterior distributions, a Markov Chain Monte Carlo algorithm is designed to compute the posterior summaries. Prediction of future record values under Bayesian and frequentist methods is also discussed mathematically and numerically. Further, a sensitivity analysis to assess the effect of prior on the estimated parameters is also a part of this study. Besides the simulation studies, the importance of the present study is illustrated with the help of a real data example. It is noted that the Bayes estimates outperform the frequentist inference
A functional autoregressive approach for modeling and forecasting short-term air temperature
Get PDFA precise forecast of atmospheric temperatures is essential for various applications such as agriculture, energy, public health, and transportation. Modern advancements in technology have led to the development of sensors and other tools to collect high-frequency air temperature data. However, accurate forecasts are challenging due to their specific features including high dimensionality, non-linearity, seasonal dependency, etc. To address these forecasting challenges, this study proposes a functional modeling framework based on the components estimation technique by partitioning the air temperature time series into deterministic and stochastic components. The deterministic component that comprises daily and yearly seasonalities is modeled and forecasted using generalized additive modeling techniques. Similarly, the stochastic component that accounts for the short-term dynamics of the process is modeled and forecasted by a functional autoregressive model, autoregressive integrated moving average, and vector autoregressive models. To evaluate the performance of models, hourly air temperature data are collected from Islamabad, Pakistan, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting results from all models are compared using the root mean squared error, mean absolute error, and mean absolute percentage error. The results suggest that the proposed FAR model performs relatively well compared to ARIMA and VAR models, resulting in lower out-of-sample forecasting errors. The findings of this research can facilitate informed decision-making across sectors, optimize resource allocation, enhance public safety, and promote socio-economic resilience
Generalized linear model based gamma control chart
No full textTraditional monitoring techniques are frequently used for monitoring a response variable, while ignoring the other important variables. A simple linear regression model to introduce covariates-based charts has received a lot of attention in the recent publications. When the response variable belongs to the exponential family, the generalized linear model (GLM) is a flexible approach to model a phenomenon. This study uses gamma distribution to introduce GLM-based Shewhart-type control charts. The monitoring statistic is developed using the Pearson residuals (PRs) obtained from the gamma regression model. The suggested charts' performance is evaluated using the run-length properties and extensive Monte Carlo simulations. A comparison of Pearson-residual to the deviance-residual charts is also discussed in this article. Finally, to emphasize the significance of the study, the proposed control charts are implemented on a real-life data set
Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach
Get PDFAir pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored in the literature, and compare it with traditional time series and machine learning models. To this end, the ozone concentration hourly time series is first filtered for yearly seasonality using smoothing splines that lead us to the stochastic (residual) component. The stochastic component is modeled and forecast using a functional autoregressive model (FAR), where each daily ozone concentration profile is considered a single functional datum. For comparison purposes, different traditional and machine learning techniques, such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), neural network autoregressive (NNAR), random forest (RF), and support vector machine (SVM), are also used to model and forecast the stochastic component. Once the forecast from the yearly seasonality component and stochastic component are obtained, both are added to obtain the final forecast. For empirical investigation, data consisting of hourly ozone measurements from Los Angeles from 2013 to 2017 are used, and one-day-ahead out-of-sample forecasts are obtained for a complete year. Based on the evaluation metrics, such as R2, root mean squared error (RMSE), and mean absolute error (MAE), the forecasting results indicate that the FAR outperforms the competitors in most scenarios, with the SVM model performing the least favorably across all cases
Process Monitoring Using Truncated Gamma Distribution
Get PDFThe time-between-events idea is commonly used for monitoring high-quality processes. This study aims to monitor the increase and/or decrease in the process mean rapidly using a one-sided exponentially weighted moving average (EWMA) chart for the detection of upward or downward mean shifts using a truncated gamma distribution. The use of the truncation method helps to enhance and improve the sensitivity of the proposed chart. The performance of the proposed chart with known and estimated parameters is analyzed by using the run length properties, including the average run length (ARL) and standard deviation run length (SDRL), through extensive Monte Carlo simulation. The numerical results show that the proposed scheme is more sensitive than the existing ones. Finally, the chart is implemented in real-world situations to highlight the significance of the proposed chart
Unit Interval Time and Magnitude Monitoring Using Beta and Unit Gamma Distributions
Get PDFQuick detection of an assignable cause is necessary for process accuracy with respect to the specifications. The aim of this study is to monitor the time and magnitude processes based on unit-interval data. To this end, maximum exponentially weighted moving average (Max-EWMA) control chart for simultaneous monitoring time and magnitude of an event is proposed. To be precise, beta and unit gamma distributions are considered to develop the Max-EWMA chart. The chart’s performance is accessed using average run length (ARL), the standard deviation of run length (SDRL), and different quantiles of the run length distribution through extensive Monte Carlo simulations. Besides a comprehensive simulation study, the proposed charting methodology is applied to a real data set. The results show that the proposed chart is more efficient in detecting small to medium-sized shifts. The results also indicate that simultaneous shifts are detected more quickly as compared to the pure shift
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