scholarly journals A Spatial-Temporal ARMA Model of the Incidence of Hand, Foot, and Mouth Disease in Wenzhou, China

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Li ◽  
Yanjun Fu ◽  
Ancha Xu ◽  
Zumu Zhou ◽  
Weiming Wang
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Spatial Autocorrelation ◽  
Arma Model ◽  
Foot And Mouth Disease ◽  
County Level ◽  
Order Difference ◽  
First Order ◽  
Proposed Model ◽  
Level Analysis

To investigate the variability of HFMD in each county of Wenzhou, a spatial-temporal ARMA model is presented, and a general Bayesian framework is given for parameter estimation. The proposed model has two advantages: (i) allowing time series to be correlated, thus it can describe the series both spatially and temporally; (ii) implementing forecast easily. Based on the HFMD data in Wenzhou, we find that HFMD had positive spatial autocorrelation and the incidence seasonal peak was between May and July. In the county-level analysis, we find that after first-order difference the spatial-temporal ARMA(0,0)×(1,0)12model provides an adequate fit to the data.

Crop Yield Estimation Using Multi-source Satellite Image Series and Deep Learning

2020 ◽  
Author(s):  
Gohar Ghazaryan ◽  
Sergii Skakun ◽  
Simon König ◽  
Ehsan Eyshi Rezaei ◽  
Stefan Siebert ◽  
...  
Keyword(s):  
Time Series ◽  
Agricultural Production ◽  
Remotely Sensed ◽  
Growth Conditions ◽  
Crop Growth ◽  
County Level ◽  
Yield Estimation ◽  
Field Level ◽  
The Impact ◽  
Level Analysis

<p>Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and extreme events. Remotely sensed time-series can be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Thus, our primary goal was to study the impact of applying different remotely sensed time series on yield estimation in U.S. at the county and field scale. Furthermore, the impact of crop growth conditions on yield variability was assessed. For county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Whereas field-level analysis was carried out using NASA’s Harmonized Landsat Sentinel-2 (HLS) product. 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R<sup>2 </sup>exceeding 0.8 when data from mid growing season were used. The results highlight the potential of yield estimation at different management scales.</p>

scholarly journals Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1576
Author(s):  
Wanhyun Cho ◽  
Sangkyuoon Kim ◽  
Myunghwan Na ◽  
Inseop Na
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Environmental Factors ◽  
Environmental Variables ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Proposed Model ◽  
Lstm Network ◽  
Statistical Time

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

Spatio-temporal clustering of hand, foot and mouth disease at the county level in Sichuan province, China, 2008–2013

2014 ◽  
Vol 143 (4) ◽  
pp. 831-838 ◽  
Author(s):  
L. LIU ◽  
X. ZHAO ◽  
F. YIN ◽  
Q. LV
Keyword(s):  
Spatial Autocorrelation ◽  
Foot And Mouth Disease ◽  
Incidence Rates ◽  
Space Time ◽  
Sichuan Province ◽  
Mouth Disease ◽  
County Level ◽  
Foot And Mouth ◽  
Control And Prevention ◽  
Spatio Temporal

SUMMARYChina has recently experienced a marked increase in the incidence of hand, foot and mouth disease (HFMD). Effective spatio-temporal monitoring of HFMD incidence is important for successful implementation of control and prevention measures. This study monitored county-level HFMD reported incidence rates for Sichuan province, China by examining spatio-temporal patterns. County-level data on HFMD daily cases between January 2008 and December 2013 were obtained from the China Information System for Disease Control and Prevention. We first conducted purely temporal and purely spatial descriptive analyses to characterize the distribution patterns of HFMD. Then, the global Moran's I statistic and space–time scan statistic were used to detect the spatial autocorrelation and identify the high-risk clusters in each year, respectively. A total of 212267 HFMD cases were reported in Sichuan province during the study period (annual average incidence 43·65/100000), and the incidence seasonal peak was between April and July. Relatively high incidence rates appeared in the northeastern–southwestern belt. HFMD had positive spatial autocorrelation at the county level with global Moran's I increasing from 0·27 to 0·52 (P < 0·001). Spatio-temporal cluster analysis detected six most-likely clusters and several secondary clusters from 2008 to 2013. The centres of the six most-likely clusters were all located in the provincial capital city Chengdu. Chengdu and its neighbouring cities had always been spatio-temporal clusters, which indicated the need for further intensive space–time surveillance. Allocating more resources to these areas at suitable times might help to reduce HFMD incidence more effectively.

scholarly journals Statistical analysis of night radiance RH using VIIRS day/night band satellite time series data

2019 ◽  
Vol 8 (3) ◽  
pp. 249
Author(s):  
Jyoti U. Devkota
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Physical Capital ◽  
Arithmetic Mean ◽  
Rate Of Change ◽  
Series Data ◽  
Observation Data ◽  
Order Difference ◽  
First Order ◽  
Night Lights

<p class="SAP-AffiliationLastline">Amount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept. This implies that the rate of change in RH with respect to change in time has more variability that the intrinsic value of RH data at the sampled point of time.</p>

scholarly journals Statistical analysis of night radiance RH using VIIRS day/night band satellite time series data

2019 ◽  
Vol 8 (1) ◽  
pp. 26
Author(s):  
Jyoti U. Devkota
Keyword(s):  
Time Series ◽  
Infrared Imaging ◽  
Time Series Data ◽  
Physical Capital ◽  
Arithmetic Mean ◽  
Series Data ◽  
Observation Data ◽  
Order Difference ◽  
First Order ◽  
Night Lights

<p class="SAP-AffiliationLastline">Amount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept. </p>

scholarly journals ANALISIS TIME SERIES MODEL ARMA UNTUK MEMPREDIKSI JUMLAH SANTRI PP SALAFIYAH SYAFI'IYAH SUKOREJO 2017-2021

2017 ◽  
Vol 11 (1) ◽  
pp. 59-86
Author(s):  
Maswar Maswar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Moving Average ◽  
Diagnostic Testing ◽  
Model Identification ◽  
Arma Model ◽  
Annual Data ◽  
Diagnostic Checking ◽  
New Students ◽  
Identification Stage

Time series analysis aims to forcasttime seriesdata in some future period based on the data in the past. The main aim of this research is to forcast the number of the new students of Salafiyah Syafi’iyahSukorejo Boarding School in Situbondo using Auto Regressive Moving Average (ARMA). This research uses annual data from 2005 until 2016. It is discusses the steps of timeseriesanlysis using the Box –Jenkinsmethod. That method comprises of several stages, they are model identification stage, parameter estimation stage, diagnostic checking and forecasting stage. Model identification stage is done by finding the model (p,q) that are considered as the most appropriate by looking at the plot of ACF and PACF of the correlogram. Parameter estimation stage is done by estimating model parameters.Whereas, Diagnostic testing and forecasting stage is done by seeing if residual estimation results is already have the quality of white noise.After the appropriate model has been identified, the next step is to use this model for forecasting. The results of this study shows that the ARMA model (2.0) provide the better forecasting results with squared the smallest value of SSR, AICand SIC.

scholarly journals Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziyang Wang ◽  
Zhijin Wang ◽  
Yingxian Lin ◽  
Jinming Liu ◽  
Yonggang Fu ◽  
...  
Keyword(s):  
Time Series ◽  
Foot And Mouth Disease ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Control And Prevention ◽  
Novel Method ◽  
Serious Disease ◽  
Combined Time Series ◽  
Emd Method ◽  
Children Under 5

Hand, foot, and mouth disease (HFMD) is an infection that is common in children under 5 years old. This disease is not a serious disease commonly, but it is one of the most widespread infectious diseases which can still be fatal. HFMD still poses a threat to the lives and health of children and adolescents. An effective prediction model would be very helpful to HFMD control and prevention. Several methods have been proposed to predict HFMD outpatient cases. These methods tend to utilize the connection between cases and exogenous data, but exogenous data is not always available. In this paper, a novel method combined time series composition and local fusion has been proposed. The Empirical Mode Decomposition (EMD) method is used to decompose HFMD outpatient time series. Linear local predictors are applied to processing input data. The predicted value is generated via fusing the output of local predictors. The evaluation of the proposed model is carried on a real dataset comparing with the state-of-the-art methods. The results show that our model is more accurately compared with other baseline models. Thus, the model we proposed can be an effective method in the HFMD outpatient prediction mission.

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