Bayesian prediction of bridge extreme stresses based on DLTM and monitoring coupled data

2019 ◽  
Vol 19 (2) ◽  
pp. 454-462
Author(s):  
Yuefei Liu ◽  
Xueping Fan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Models ◽  
Linear Trend ◽  
Bayesian Probability ◽  
Dynamic Linear Models ◽  
Extreme Stress ◽  
Local Polynomial ◽  
Series Analysis ◽  
Polynomial Theory

For predicting dynamic coupled extreme stresses of bridges with monitoring coupled data, this article considers monitoring extreme stress data as a time series, and takes into account its coupling generated by the fusion of non-stationarity and randomness. First, the local polynomial theory is introduced, and the local polynomial order of monitoring coupled extreme stress data is estimated with time-series analysis method. Second, based on time-series analysis results, dynamic linear trend models (DLTM) and the corresponding Bayesian probability recursive processes are given to predict dynamic coupled extreme stresses. Finally, through the illustration of monitoring coupled extreme stress data from an actual bridge, the proposed method, which is compared with the traditional Bayesian dynamic linear models, is proved to be more effective for predicting dynamic coupled extreme stresses of bridges.

scholarly journals TIME SERIES ANALYSIS OF SURFACE DEFORMATION OF BENGALURU CITY USING SENTINEL-1 IMAGES

2018 ◽  
Vol IV-5 ◽  
pp. 473-477
Author(s):  
N. Ittycheria ◽  
D. S. Vaka ◽  
Y. S. Rao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Deformation ◽  
Linear Trend ◽  
Polarization Data ◽  
Persistent Scatterer Interferometry ◽  
Advanced Technique ◽  
Interferometric Technique ◽  
Series Analysis ◽  
Persistent Scatterers

<p><strong>Abstract.</strong> Persistent Scatterer Interferometry (PSI) is an advanced technique to map ground surface displacements of an area over a period. The technique can measure deformation with a millimeter-level accuracy. It overcomes the limitations of Differential Synthetic Aperture Radar Interferometry (DInSAR) such as geometric, temporal decorrelation and atmospheric variations between master and slave images. In our study, Sentinel-1A Interferometric Wide Swath (IW) mode descending pass images from May 2016 to December 2017 (23 images) are used to identify the stable targets called persistent scatterers (PS) over Bengaluru city. Twenty-two differential interferograms are generated after topographic phase removal using the SRTM 30 m DEM. The main objective of this study is to analyze urban subsidence in Bengaluru city in India using the multi-temporal interferometric technique such as PSI. The pixels with Amplitude Stability Index &amp;geq;<span class="thinspace"></span>0.8 are selected as initial PS candidates (PSC). Later, the PSCs having temporal coherence &amp;gt;<span class="thinspace"></span>0.5 are selected for the time series analysis. The number of PSCs that are identified after final selection are reduced from 59590 to 54474 for VV polarization data and 15611 to 15596 for VH polarization data. It is interesting to note that a very less number of PSC are identified in cross-polarized images (VH). A high number of PSC have identified in co-polarized (VV) images as the vertically oriented urban targets produce a double bounce, which results in a strong return towards the sensor. The velocity maps obtained using VV and VH polarizations show displacement in the range of &amp;plusmn;<span class="thinspace"></span>20<span class="thinspace"></span>mm<span class="thinspace"></span>year<sup>&amp;minus;1</sup>. The subsidence and the upliftment observed in the city shows a linear trend with time. It is observed that the eastern part of Bengaluru city shows more subsidence than the western part.</p>

Time series analysis of variation in abundance of selected cocoa insects and fitting of simple linear predictive models

1993 ◽  
Vol 83 (2) ◽  
pp. 153-169 ◽  
Author(s):  
M Bigger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Models ◽  
Extension Growth ◽  
Atmospheric Moisture ◽  
Series Analysis ◽  
Auto Correlation ◽  
Autoregressive Parameter ◽  
Cyclical Behaviour ◽  
Analysis Of Variation

AbstractA plot of cocoa trees at the Cocoa Research Institute, Tafo, Ghana, was inspected weekly for the presence on each tree of 18 species of insects. The 306 consecutive weekly records of percentage of trees occupied by each species were subjected to time series analysis. Auto-correlation and partial auto-correlation functions were calculated for each series and used to identify simple autoregressive linear models to account for the serial correlation. It was found that all species needed a stabilizing autoregressive parameter of lag 1 and all but four a second autoregressive parameter of lag 2. Seasonal autoregressive parameters at lags 3, 4, 5, 7, 9, or 13 were needed for over half the species, either in addition to the parameter at lag 2 or in place of it. It is postulated that these seasonal parameters mimic generation cycles. Runs of the models using random inputs produced series which were close to the originals in general form. The models could be further refined by adjusting the fixed mean levels assumed by the models to take into account the effects due to the abundance of young extension growth on the trees and atmospheric moisture, as measured by afternoon relative humidity readings. Although the production of extension growth is cyclical it would seem that it does not induce the cyclical behaviour observed in some of the insect series. The peaks in the latter cycles are, however, reinforced when they coincide with peaks in extension growth.

scholarly journals Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1
Author(s):  
Debasis Mithiya ◽  
Kumarjit Mandal ◽  
Simanti Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Analysis ◽  
Linear Model ◽  
Agricultural Production ◽  
Linear Models ◽  
Monthly Rainfall ◽  
Series Analysis ◽  
Non Linear

Indian agriculture depends heavily on rainfall. It not only influences agricultural production but also affects the prices of all agricultural commodities. Rainfall is an exogenous variable which is beyond farmers’ control. The outcome of rainfall fluctuation is quite natural. It has been observed that fluctuation in rainfall brings about fluctuation in output leading to price changes. Considering the importance of rainfall in determining agricultural production and prices, the study has attempted to forecast monthly rainfall in India with the help of time series analysis using monthly rainfall data. Both linear and non-linear models have been used. The value of diagnostic checking parameters (MAE, MSE, RMSE) is lower in a non-linear model compared to a linear one. The non-linear model - Artificial Neural Network (ANN) has been chosen instead of linear models, namely, simple seasonal exponential smoothing and Seasonal Auto-Regressive Integrated Moving Average to forecast rainfall. This will help to identify the proper cropping pattern.

scholarly journals Was School Closure Effective in Mitigating Coronavirus Disease 2019 (COVID-19)? Time Series Analysis Using Bayesian Inference

2020 ◽  
Author(s):  
Kentaro Iwata ◽  
Asako Doi ◽  
Chisato Miyakoshi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Trend ◽  
Credible Interval ◽  
School Closure ◽  
Sensitivity Analyses ◽  
Series Analysis ◽  
Daily Data ◽  
Coronavirus Infection ◽  
Novel Coronavirus

Background: Coronavirus disease 2019 (COVID-19) pandemic are causing significant damages to many nations. For mitigating its risk, Japan&rsquo;s Prime Minister called on all elementary, junior high and high schools nationwide to close beginning March 1, 2020. However, its effectiveness in decreasing disease burden has not been investigated. Methods: We used daily data on the report of COVID-19 and coronavirus infection incidence in Japan until March 31, 2020. Time series analysis were conducted using Bayesian method. Local linear trend models with interventional effect were constructed for number of newly reported cases of COVID-19, including asymptomatic infections. We considered that the effects of intervention start to appear 9 days after the school closure; i.e., on March 9. Results: The intervention of school closure did not appear to decrease the incidence of coronavirus infection. If the effectiveness of school closure began on March 9, mean coefficient &alpha; for effectiveness of the measure was calculated to be 0.08 (95% credible interval -0.36 to 0.65), and the actual reported cases were more than predicted, yet with rather wide credible interval. Sensitivity analyses using different dates also showed similar results. Conclusions: School closure carried out in Japan did not show the effectiveness to mitigate the transmission of novel coronavirus infection.

Time series analysis in forecasting pririrodno focal infections

2015 ◽  
Vol 9 (4) ◽  
pp. 0-0
Author(s):  
Евстегнеева ◽  
V. Evstegneeva ◽  
Честнова ◽  
Tatyana Chestnova ◽  
Смольянинова ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Bank Vole ◽  
Linear Trend ◽  
Random Variables ◽  
Vole Population ◽  
Long Term Effects ◽  
Series Analysis

Mathematical methods and models used in forecasting problems may relate to a wide variety of topics: from the regression analysis, time series analysis, formulation and evaluation of expert opinions, simulation, systems of simultaneous equations, discriminant analysis, logit and probit models, logical unit decision functions, variance or covariance analysis, rank correlation and contingency tables, etc. In the analysis of the phenomenon over a long timeperiod, for example, the incidence of long-term dynamics with a forecast of further development of the process, you should use the time series, which is influenced by the following factors: • Emerging trends of the series (the trend in cumulative long-term effects of many factors on the dynamics of the phenomenon under study - ascending or descending); • forming a series of cyclical fluctuations related to the seasonality of the disease; • random factors. In our study, we conducted a study to identify cyclical time series of long-term dynamics of morbidity of HFRS and autumn bank vole population. This study was performed using the autocorrelation coefficient. As a result of time-series studies of incidence of HFRS, indicators autumn bank vole population revealed no recurrence, and these figures are random variables, which is confirmed by three tests: nonrepeatability of time series, the assessment increase and decrease time-series analysis of the sum of squares. This shows that a number of indicators of the time series are random variables, contains a strong non-linear trend, to identify which need further analysis, for example by means of regression analysis.

scholarly journals A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

2010 ◽  
Vol 31 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Philip M. Polgreen ◽  
Ming Yang ◽  
Lucas C. Bohnett ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Linear Trend ◽  
International Classification Of Diseases ◽  
Series Data ◽  
Series Analysis ◽  
Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

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