Change point determination for a multivariate process using a two-stage hybrid scheme

Shao, Yuehjen E.; Hou, Chia-Ding

doi:10.1016/j.asoc.2012.02.008

Cited by 28 publications

(25 citation statements)

References 33 publications

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“…However, one difficulty in classification design that will be encountered is that the number of categories of the classifiers' output nodes will increase when more types of MCCPs are involved in an MIMO system. Hybrid modeling techniques [31,32] and/or other ML techniques, such as artificial immune systems and random forests, may be worthy of implementation to decrease the number of output categories in the future.…”

Section: Discussionmentioning

confidence: 99%

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Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Shao

2020

Mathematics

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A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective recognition of CCPs has received considerable attention in recent years for their potential use in improving process quality. However, most studies have focused on the recognition of CCPs for SPC applications alone. Specifically, even though numerous studies have addressed the increased use of the SPC and engineering process control (EPC) mechanisms, very little research has discussed the recognition of CCPs for multiple-input multiple-output (MIMO) systems. It is much more difficult to recognize the CCPs of an MIMO system since two or more disturbances are simultaneously involved in the process. The purpose of this study is thus to propose several machine learning (ML) classifiers to overcome the difficulties in recognizing CCPs in MIMO systems. Because of their efficient and fast algorithms and effective classification performance, the considered ML classifiers include an artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and multivariate adaptive regression splines (MARS). Furthermore, one problem may arise due to the existence of embedded mixture CCPs (MCCPs) in MIMO systems. In contrast to using typical process outputs alone in a classifier, this study employs both process outputs and EPC compensation to ensure the effectiveness of CCP recognition. Experimental results reveal that the proposed classifiers are able to effectively recognize MCCPs for MIMO systems.

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Section: Discussionmentioning

confidence: 99%

“…The reason for using ELM is that it has the advantages of fast learning speed and good generalization performance [29,30]. The reason for choosing MARS is that it has been adopted for CCP recognition in only a few studies, although MARS is effective in classification [31,32].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Shao

2020

Mathematics

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“…The hidden nodes were chosen as 22, 23, 24, 25 and 26 for the second design. Since the learning rate of 0.01 is a very effective setting [22], [28], this study sets the values of learning setting as 0.01 for ANN modeling. After performing the ANN modeling, the first design obtained that the {2-6-1} and {2-2-1} structures provided the best results and a minimum testing MAPE for IE and CE sales, respectively.…”

Section:  Nmentioning

confidence: 99%

Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Shao¹,

Tsai²

2018

IJEEEE

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Electricity is one of the most important sources of energy on earth. Today, electricity has become a part of our life. Electricity is the key component to modern technology and without it most of the products that we use simply could not work. Without doubt, the economic growth for almost every country in the world is affected by electricity rates. Therefore, the prediction of electricity sales is very important for Taiwanese economy. This study employs the autoregressive integrated moving average (ARIMA), artificial neural networks (ANN) and the integrated ARIMA-ANN approaches for predicting the industrial electricity and commercial electricity sales (IECES) in Taiwan. The forecasting accuracy measure is based on the mean absolute percentage error. The real dataset, from the years 2006 to 2016, for IECES in Taiwan are collected and analyzed. The prediction results show that the ARIMA-ANN model has the most satisfactory forecasting accuracy for predictions of IECES in Taiwan.

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“…Finally, the reason for using the hybrid ARIMA-ANN and ARIMA-MARS is that they possess linear and nonlinear forecasting abilities. The fundamental concept of the hybrid forecasting scheme is to capture various characteristics in the data by making use of individual model's dominance [39,40]. Therefore, the hybrid technique can improve forecasting accuracy using each model's unique features.…”

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confidence: 99%

Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Shao

Tsai

2018

Energies

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Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models.

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Change point determination for a multivariate process using a two-stage hybrid scheme

Cited by 28 publications

References 33 publications

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Predictions of Industrial and Commercial Electricity Sales in Taiwan Using ARIMA and Artificial Neural Networks Techniques

Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

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