On the Performance of the Mixed Seasonal Autoregressive Integrated Moving Average One-Dimensional Bilinear Time series model

2020 ◽  
Vol 7 (3) ◽  
pp. 127-140
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Time Series Model ◽  
Autoregressive Integrated Moving Average ◽  
One Dimensional

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Joinpoint Regression Analysis ◽  
Infectious Disease Reporting

Abstract Background The objective was to identify the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the China CDC. Joinpoint regression analysis was used to observe the trend. The monthly incidence was predicted by the Seasonal autoregressive integrated moving average (SARIMA) model. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission. Results We included 13,991,850 TB cases from 2004 to 2017. The final selected model was the 0 Joinpoint model with an annual average percent change of -3.3. A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March. The best SARIMA model was (0, 1, 1) X (0, 1, 1) 12 , with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences areas were mainly affected by the autoregressive component for the past fourteen years. Conclusion A significant decreasing trend was seen from 2004 to 2017. The seasonal peaks were in January and March every year. Obvious clusters were identified in Tibet and Xinjiang Province. A spatial heterogeneity in the component driving the transmission of TB was identified from the multivariate time series model. This suggested that targeted preventive efforts should be made in different provinces based on the main component contributing to the epidemics.

scholarly journals Prediksi Pencurian Sepeda Motor Menggunakan Model Time Series (Studi Kasus: Polres Kotabumi Lampung Utara)

2020 ◽  
Vol 14 (3) ◽  
pp. 425-434
Author(s):  
MELI PRANATA ◽  
DIAN ANGGRAINI ◽  
Deden Makbuloh ◽  
Achi Rinaldi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Model ◽  
Ar Model ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Tindak kriminal adalah kejahatan yang melanggar undang-undang suatu Negara atau melanggar norma yang berlaku dalam masyarakat. Pencurian merupakan salah satu bentuk dari perbuatan tindak kriminal. Dampak yang ditimbulkan dari adanya pencurian adalah perasaan kurang aman, takut, dan tenang. Salah satu model yang digunakan untuk memprediksi jumlah kasus pencurian yaitu model time series. Model time series adalah serangkaian nilai pengamatan yang diambil selama periode waktu tertentu. Pada umumnya, dalam interval-interval yang sama panjang, (Spuege & Stephens, 2004). Penelitian ini bertujuan memodelkan data tindak kriminal yang terjadi di Lampung Utara dengan model Autoregressive (AR), Moving Average (MA), dan Autoregressive Integrated Moving Average (ARIMA). Selanjutnya dari model terbaik akan digunakan untuk peramalan 6 bulan kedepan. Hasil penelitian model AR , model AR , model MA , ARIMA , dan model ARIMA . Model MA  memiliki koefisien parameter yang signifikan, memenuhi uji diagnostic tidak adanya residual pada model dan memiliki nilai RMSE dan AIC terkecil dengan nilai RMSE sebesar dan nilai AIC sebesar . Hasil prediksi model MA  untuk 6 bulan ke depan cenderung mendatar.

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Leading Role ◽  
Joinpoint Regression Analysis

Abstract Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. Conclusion In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Autoregressive integrated moving average time series model for forecasting air pollution in Nanded city, Maharashtra, India

2018 ◽  
Vol 4 (4) ◽  
pp. 1435-1444 ◽  
Author(s):  
Govind Eknath Kulkarni ◽  
Aniket Avinash Muley ◽  
Nilesh Kailas Deshmukh ◽  
Parag Upendra Bhalchandra
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Moving Average ◽  
Time Series Model ◽  
Autoregressive Integrated Moving Average

scholarly journals RESEARCH ON THE COMBUSTION PROCESS USING TIME SERIES

2020 ◽  
Vol 10 (2) ◽  
pp. 52-55
Author(s):  
Żaklin Grądz
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Combustion Process ◽  
Measurement Data ◽  
Stationary Time Series ◽  
Autoregressive Integrated Moving Average ◽  
One Dimensional ◽  
And Control ◽  
Stationary Form

In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average
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