Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.

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Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

Epidemiology and Infection ◽

10.1017/s095026881800362x ◽

2019 ◽

Vol 147 ◽

Cited By ~ 4

Author(s):

C. W. Tian ◽

H. Wang ◽

X. M. Luo

Keyword(s):

Time Series ◽

Seasonal Variation ◽

Moving Average ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Annual Peak ◽

Time Series Modelling ◽

Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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Time-Series Statistical Model for Forecasting Revenue and Risk Management

Journal of Student Research ◽

10.47611/jsrhs.v10i3.2050 ◽

2021 ◽

Vol 10 (3) ◽

Author(s):

Mihir Kelkar ◽

Cosmin Borsa ◽

Lina Kim

Keyword(s):

Time Series ◽

Low Cost ◽

Moving Average ◽

Business Strategies ◽

Annual Reports ◽

Fiscal Year ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Low Cost Carrier ◽

Southwest Airlines

Following a Low-Cost Carrier (LCC) model, Southwest Airlines has consistently demonstrated growing annual revenues up until the start of the COVID-19 pandemic. Southwest’s quarterly revenue shows that there exists a strong seasonal component with the revenue in the first quarter of the fiscal year (September) significantly higher than other quarters. Using the quarterly revenue data we constructed a time-series model: a seasonal autoregressive integrated moving average (SARIMA) model to forecast Southwest’s revenue over 2020. We then performed a cost and solvency risk analysis using the company’s financial results from its annual reports to analyze Southwest’s financial performance due to COVID-19, and proposed business strategies to keep Southwest financially stable.

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The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

International Research in Higher Education ◽

10.5430/irhe.v4n3p58 ◽

2019 ◽

Vol 4 (3) ◽

pp. 58

Author(s):

Lu Qin ◽

Kyle Shanks ◽

Glenn Allen Phillips ◽

Daphne Bernard

Keyword(s):

Time Series ◽

Historical Data ◽

Moving Average ◽

Arima Model ◽

Forecasting Accuracy ◽

Autoregressive Integrated Moving Average ◽

Enrollment Forecasting ◽

Moving Average Model ◽

The Impact ◽

Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

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Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

Academic Journal of Research and Scientific Publishing ◽

10.52132/ajrsp/en.2231 ◽

2021 ◽

Vol 2 (23) ◽

pp. 1-15

Author(s):

Mwana Said Omar ◽

◽

Hajime Kawamukai

Keyword(s):

Time Series ◽

Arid Region ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Moving Average ◽

Additive Model ◽

Grid Search ◽

Sarima Model ◽

Ndvi Time Series ◽

Sarima Models

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.

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

10.21203/rs.3.rs-17053/v1 ◽

2020 ◽

Author(s):

Zhongbao Zuo ◽

Miaochan Wang ◽

Huaizhong Cui ◽

Ying Wang ◽

Jing Wu ◽

...

Keyword(s):

Time Series ◽

Regression Analysis ◽

Moving Average ◽

Multivariate Time Series ◽

Joinpoint Regression ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

The Past ◽

Leading Role ◽

Joinpoint Regression Analysis

Abstract BackgroundChina 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.MethodsThe 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 analysis was performed to detect. The heterogeneous transmission of TB was detected by the multivariate time series model. ResultsWe 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. ConclusionIn 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 spatial heterogeneity 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.

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Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v23n5p324-329 ◽

2019 ◽

Vol 23 (5) ◽

pp. 324-329

Author(s):

Roberto L. da S. Carvalho ◽

Angel R. S. Delgado

Keyword(s):

Time Series ◽

Reference Evapotranspiration ◽

Moving Average ◽

Group Method ◽

Data Handling ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Irrigation Methods ◽

Artificial Neural Network Ann ◽

Climatic Time Series

ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.

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

10.21203/rs.3.rs-17053/v3 ◽

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.

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STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta

International Journal on Information and Communication Technology (IJoICT) ◽

10.21108/ijoict.v7i2.553 ◽

2021 ◽

Vol 7 (2) ◽

pp. 1-10

Author(s):

Agnes Zahrani ◽

Aniq A. Rohmawati ◽

Siti Sa’adah

Keyword(s):

At Risk ◽

Value At Risk ◽

Extreme Values ◽

Moving Average ◽

Extreme Value ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Increment Rate ◽

The Difference ◽

Parameter Estimators

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

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

10.21203/rs.3.rs-17053/v2 ◽

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.

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Time series analysis and forecasting of chlamydia trachomatis incidence using surveillance data from 2008 to 2019 in Shenzhen, China

Epidemiology and Infection ◽

10.1017/s0950268820000680 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 2

Author(s):

R. X. Weng ◽

H. L. Fu ◽

C. L. Zhang ◽

J. B. Ye ◽

F. C. Hong ◽

...

Keyword(s):

Time Series ◽

Chlamydia Trachomatis ◽

Moving Average ◽

Seasonal Fluctuation ◽

Percentage Error ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Shenzhen City ◽

Control And Prevention

Abstract Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.

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