Evaluating the healthcare practice by defining healthcare principles: An autoregressive integrated moving average model based on time series

2020 ◽  
Author(s):  
Jingjing Yan
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Model Based ◽  
Healthcare Practice ◽  
Moving Average Model

scholarly journals Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis

2021 ◽  
Vol 0 ◽  
pp. 1-9
Author(s):  
Vera Gregório ◽  
Dinilson Pedroza ◽  
Celivane Barbosa ◽  
Gilberto Bezerra ◽  
Ulisses Montarroyos ◽  
...  
Keyword(s):  
Time Series ◽  
Information System ◽  
Moving Average ◽  
Northeastern Brazil ◽  
Average Model ◽  
Detection Rates ◽  
Autoregressive Integrated Moving Average ◽  
Notifiable Diseases ◽  
Hyperendemic Area ◽  
Moving Average Model

Background: Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series. Aim: To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil. Methods: A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box–Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020. Results: A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020. Limitations: This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported. Conclusion: The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

scholarly journals Network Traffic Modeling in a Wi-Fi System with Intelligent Soil Moisture Sensors (WSN) Using IoT Applications for Potato Crops and ARIMA and SARIMA Time Series

2020 ◽  
Vol 10 (21) ◽  
pp. 7702
Author(s):  
Alfonso José López Rivero ◽  
Carlos Andrés Martínez Alayón ◽  
Roberto Ferro ◽  
Daniel Hernández de la Iglesia ◽  
Vidal Alonso Secades
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Moving Average ◽  
Specific Area ◽  
Data Traffic ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Soil Moisture Sensors ◽  
Moving Average Model ◽  
Potato Crops

This article presents the results obtained by analyzing the data traffic that originated in a system with intelligent soil moisture sensors (Wireless Sensor Network—WSN) that transmit through a wireless network. This study sought to integrate smart agriculture and IoT (Internet of Things) applications in potato crops in various rural settings. Using these measurements, the data analysis was performed through the ARIMA (autoregressive integrated moving average model) and SARIMA (seasonal autoregressive integrated moving average model) time series following the Box–Jenkins methodology. GRETL (Gnu Regression, Econometrics and Time-series Library) free software was used to generate a teletraffic behavior prediction model in a larger-scale implementation. The main objective was the creation of a model that allows an analysis and simulation about the behavior of the main performance parameters that a medium-scale WSN system would have for the monitoring of a crop. Thanks to this analysis, it will be possible to determine the technical characteristics that a sensor deployment should have in a specific area and for a specific crop.

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