Model Identification of Integrated ARMA Processes

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

doi:10.1080/00273170701836646

Cited by 10 publications

(11 citation statements)

References 32 publications

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“…Specifically, a SARIMA(1,0,1)x(1,0,1) 52 model was used to analyze the outpatient time series, a SARIMA(1,0,3)x(1,0,1) 52 model was used to analyze the emergency department data, and a SARIMA(1,0,1)x(1,0,1) 52 model was used to analyze the inpatient data for patients with acute respiratory failure. The autoregressive and moving-average orders were identified by both SCAN and extended sample autocorrelation function (ESACF) methods [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

Elmore¹,

Wang²,

Kerr³

et al. 2020

J Med Internet Res

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Background Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. Objective The aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. Methods A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. Results The percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. Conclusions A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

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Section: Methodsmentioning

confidence: 99%

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

Elmore¹,

Wang²,

Kerr³

et al. 2020

J Med Internet Res

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“…In other words, only an elaborated strategy combining different methods could ensure accurate model selection. Based on the results of the present study, Stadnytska, Braun, and Werner (2007) developed a methodology for model selection combining objective and subjective techniques and demonstrated its usefulness on empirical data.…”

Section: Discussionmentioning

confidence: 99%

Comparison of automated procedures for ARMA model identification

2008

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This article evaluates the performance of three automated procedures for ARMA model identification commonly available in current versions of SAS for Windows: MINIC, SCAN, and ESACF. Monte Carlo experiments with different model structures, parameter values, and sample sizes were used to compare the methods. On average, the procedures either correctly identified the simulated structures or selected parsimonious nearly equivalent mathematical representations in at least 60% of the trials conducted. For autoregressive models, MINIC achieved the best results. SCAN was superior to the other two procedures for mixed structures. For moving-average processes, ESACF obtained the most correct selections. For all three methods, model identification was less accurate for low dependency than for medium or high dependency processes. The effect of sample size was more pronounced for MIMIC than for SCAN and ESACF. SCAN and ESACF tended to select higherorder mixed structures in larger samples. These fmdings are confined to stationary nonseasonal time series.Time series analysis deals with repeated and equally spaced observations on a single unit. Classical statistical techniques are no longer appropriate here because data points cannot be assumed independent and uncorrelated. One of the most widely employed procedures for time series data is the autoregressive integrated moving-average (ARIMA) approach proposed by Box and Jenkins (1970). Since Glass, Willson, and Gottman (1975) introduced the ARIMA technique to the social and behavioral sciences, this methodology has been increasingly employed in different research fields (Delcor,

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“…Fractal characteristics or long memory processes can be measured via different algorithms, each having its own statistical parameter. Here, the difficulty arises from the fact that, for each parameter, numerous estimators have been defined, but the effectiveness of each is still debated in the literature (Stadnitski, 2012;Stadnytska et al, 2010). Studies often focus on one or few estimators without a rigorous reason for comparing them.…”

Section: Type Of Signalmentioning

confidence: 99%

“…Methods for H estimation differ depending on the signal class of the original sequence, which can be either Fractional Gaussian noise (fGn) or fractional Brownian motion (fBm) (Mandelbrot and Van Ness, 1968). fGn is stationary with constant variance and mean whereas fBm is nonstationary, even if both signals are theoretically linked: differencing fBm produces fGn and integrating fGn creates fBm (Stadnytska et al, 2010). The same original sequence expressed as one or the other signal class will be characterized by the same Hurst exponent (Figure 2).…”

Section: Type Of Signalmentioning

confidence: 99%

“…Where 6 4 ( ) is the regression estimate for 4 ( ) at each box size n and F(n) is the fluctuation of the modified root-mean-square equation across all scales available (2 2 , 2 3 , 2 4 … 2 m ) where m is the largest scale examined, such as 2 m < N/2 (rounded down to the nearest whole number) (Constantine and Percival, 2007;Stadnytska et al, 2010;Stroe-Kunold et al, 2009). We then obtained the following relationship…”

Section: Detrended Fluctuation Analysis (Dfa)mentioning

confidence: 99%

See 1 more Smart Citation

To draw or not to draw: understanding the temporal organization of drawing behaviour using fractal analyses

Beltzung¹,

Martinet²,

MacIntosh

et al. 2021

Preprint

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Studies on drawing often focused on spatial aspects of the finished products. Here, the drawing behaviour was studied by analysing its intermittent process, between drawing (i.e. marking a surface) and interruption (i.e. a pause in the marking gesture). To assess how this intermittence develops with age, we collected finger-drawings on a touchscreen by 185 individuals (children and adults). We measured the temporal structure of each drawing sequence to determine its complexity. To do this, we applied temporal fractal estimators to each drawing time series before combining them in a Principal Component Analysis procedure. The youngest children (3 years-old) drew in a more stereotypical way with long-range dependence detected in their alternations between states. Among older children and adults, the complexity of drawing sequences increased showing a less predictable behaviour as their drawings become more detailed and figurative. This study improves our understanding of the temporal aspects of drawing behaviour, and contributes to an objective understanding of its ontogeny.

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Model Identification of Integrated ARMA Processes

Cited by 10 publications

References 32 publications

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

Comparison of automated procedures for ARMA model identification

To draw or not to draw: understanding the temporal organization of drawing behaviour using fractal analyses

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