Control Chart as Verification Tools  in Time Series Model

Imro'ah, Nurfitri; Huda, Nur’ainul Miftahul

doi:10.30598/barekengvol16iss3pp995-1002

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The method idea in this section originates from the control chart as verification tools in the time series model proposed by Imro'ah N and Huda N M [7] in 2022. It consists of an I control chart and an MR control chart, which represent a single observed value and moving range, this control chart can be used to measure the accuracy of a time series model.…”

Section: Control Chartmentioning

confidence: 99%

Research on high-dimensional complex data monitoring based on quantile regression

Lu,

2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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Quantile regression is a technique used to estimate the conditional quantile of the response variable. Unlike the classical least squares method, it doesn't assume any specific error distribution and offers a more comprehensive assessment of the impact of independent variables on the conditional distribution of the response variable. Consequently, quantile regression boasts unique advantages in practical applications. In this study, we employ the quantile regression method to determine coefficients in the time series model. By processing time series data using quantile regression and monitoring it alongside control charts, we aim to validate the approach using real-world examples of population and birth rate data in our country.

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Section: Control Chartmentioning

confidence: 99%

Research on high-dimensional complex data monitoring based on quantile regression

Lu,

2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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“…Control charts for time series models can be applied to any model, assuming that residuals are white noise. If these conditions are satisfied, the calculations can be represented on an IMR control chart[16]. As a control chart, this study employs the IMR control chart.…”

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confidence: 99%

Arima Model Verification With Outlier Factors Using Control Chart

Umairah,

Imro'ah,

Huda

2024

BAREKENG: J. Math. & App.

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Control charts are often used in quality control processes, especially in the industrial sector because of their significant benefits in increasing industrial production. However, control charts can also be used throughout the field of time series modeling to evaluate measures of accuracy represented by a particular time series model. The application of control charts in this research meets the criteria for evaluating accuracy. However, it is not certain that the time series model will have a high level of accuracy. There are various factors that can influence this phenomenon, one of which is the potential for outliers. Therefore, it is very important to perform time series modeling by adding an outlier factor. The residuals of the time series model obtained are used to create a control chart for model verification. The aim of this research is to evaluate the validity of time series models by looking at the influence of outlier characteristics to improve their accuracy. This research studies the accuracy of a time series model built using Gross Domestic Product (GDP) data in Indonesia. There are two different models, namely the ARIMA model without outlier factors and the ARIMA model with outlier factors which are used for research purposes. Both models were performed using the same data set. The results of this study indicate that the ARIMA model with outlier factors has better accuracy than the ARIMA model without outlier factors. This conclusion can be drawn based on the observation that the residual value is within the predetermined control limits, thus indicating that the process is in a state of statistical control.

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Control Chart as Verification Tools in Time Series Model

Cited by 2 publications

References 9 publications

Research on high-dimensional complex data monitoring based on quantile regression

Research on high-dimensional complex data monitoring based on quantile regression

Arima Model Verification With Outlier Factors Using Control Chart

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