Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike’s information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.     

Artificial neural network and time series models for predicting soil salt and water content

Volumetric water content of a silt loam soil (fluvo-aquic soil) in North China Plain was measured in situ by L-520 neutron probe (made in China) at three depths in the crop rootzone during a lysimeter experiment from 2001 to 2006. The electrical conductivity of the soil water (EC_sw) was measured by salinity sensors buried in the soil during the same period at 10, 20, 45 and 70 cm depth below soil surface. These data were used to test two mathematical procedures to predict water content and soil water salinity at depths of interest: all the available data were divided into training and testing datasets, then back propagation neural networks (BPNNs) were optimized by sensitivity analysis to minimizing the performance error, and then were finally used to predict soil water and EC_sw. In order to meet with the prerequisite of autoregressive integrated moving average (ARIMA) model, firstly, original soil water content and EC_sw time series were likewise transformed to obtain stationary series. Subsequently, the transformed time series were used to conduct analysis in frequency domain to obtain the parameters of the ARIMA models for the purposes of using the ARIMA model to predict soil water content and EC_sw. Based on the statistical parameters used to assess model performance, the BPNN model performed better in predicting the average water content than the ARIMA model: coefficient of determination (R²) = 0.8987, sum of squares error (SSE) = 0.000009, and mean absolute error (MAE) = 0.000967 for BPNN as compared to R² = 0.8867, SSE = 0.000043, MAE = 0.002211 for ARIMA. The BPNN model also performed better than the ARIMA model in predicting average EC_sw of soil profile. However, the ARIMA model performed better than the BPNN models in predicting soil water content at the depth of 20 cm and EC_sw at the depth of 10 cm below soil surface. Overall, the model developed by BPNN network showed its advantage of less parameter input, nonlinearity, simple model structure and good prediction of soil EC_sw and water content, and it gave an alternative method in forecasting soil water and salt dynamics to those based on deterministic models based on Richards’ equation and Darcy's law provided climatic, cropping patterns, salinity of the irrigation water and irrigation management are very similar from one year to the next.     

农业机械总动力的ARMA和方程法组合预测分析——以青岛市为例

农业机械总动力是反映和评价农业机械化水平的一个重要指标,精准的预测农业机械总动力具有非常重要的意义。本文根据青岛地区1990~2008年的农用机械总动力历史数据的变化形态,找到合适的方程提取确定性趋势,并运用自回归移动平均模型ARMA(p,q)及其建模思路,结合Eviews软件构建了ARMA(1,1)模型。经检验此模型预测精度较高,拟合效果理想,进一步说明了方程法和ARMA组合模型用于对农业机械总动力预测的可行性,可以为相关部门和单位的预测工作提供一定借鉴。     

ARIMA模型在云南省农村居民人均消费预测中的应用

运用1978～2007年云南省农村居民人均消费支出数据建立了ARIMA（3，1，0）模型，其预测结果通过了检验，预测结果为各级政府提出扩大农村消费支出的政策提供了科学依据。     

中国居民消费价格指数预测

利用1994—2013年中国居民消费价格指数的统计数据,建立ARIMA模型并对2014年中国居民消费价格指数进行预测分析。结果显示,建立的模型预测误差较小,可以对居民消费价格指数进行有效短期预测。以此模型对2014年1—12月居民消费价格指数进行预测,结果表明未来1年物价总水平基本稳定,居民消费价格指数同比涨幅将在3.0%以内。     

基于季节ARIMA模型的铜陵市气温序列的预报

运用EVIEWS软件,对铜陵市48年来的月平均气温时间序列进行统计分析,并对该动态数据进行建模和预测。采用差分方法对样本数据进行预处理,然后定阶,并进行参数估计,建立季节ARIMA模型对铜陵市气温数据进行预报。预报结果显示,季节ARIMA模型的平均绝对误差值为0.875。将ARIMA模型预报结果与径向基(radial basis function,RBF)神经网络模型的预报值比较可知,其预报结果优于RBF神经网络的预测结果。     

内蒙古能源消费总量的研究——基于ARIMA疏系数模型

本文以《内蒙古统计年鉴2010》中1985-2009年内蒙古能源消费总量的数据为依据,运用时间序列的建模方法采用SAS 9.0软件建立ARIMA疏系数模型对内蒙古未来能源消费总量作出预测.结果显示,模型预测效果较好.     

洛惠渠灌区地下水动态敏感因子研究

以陕西省洛惠渠灌区实测数据为例,首先采用多元时空序列马尔可夫链分析模型,对多年来洛惠渠灌区地下水动态进行趋势因子评价分析;采用改进的灰色斜率关联法分析各影响因子与地下水埋深的敏感关系;提出临界蒸发量这一概念;建立ARIMA动态模型,并对蒸发量进行预测.结果表明,该灌区地下水动态趋势较恶劣;蒸发量是影响该灌区地下水动态的最敏感因子,各因子之间相互作用,形成了复杂条件下的耦合关系;当蒸发量超过1 800 mm临界值时,地下水位开始回升,对土壤有盐碱化的威胁;未来几年蒸发量多数超过临界值,模型预测精度较高.将以上方法体系运用到灌区地下水动态研究中是切实可行的,可统一管理和调控水资源提供科学依据.     

基于指数平滑和ARIMA模型的西北地区饱和水汽压差预测

饱和水汽压差是土壤-植被-大气连续体水分传输过程的关键影响因素,在全球气候变化背景下,预测西北地区饱和水汽压差,对于植被恢复和农林业气象灾害风险评估具有重要的现实意义。基于西北五省(区)1990—2019年月饱和水汽压差值,采用趋势分析和小波分析等方法研究了西北地区饱和水汽压差年际变化特征和周期性变化规律;采用指数模型和ARIMA模型,筛选最佳样本步长和预测步长,对西北地区饱和水汽压差进行模拟和预测。结果表明:(1)西北五省(区)中,新疆年均饱和水汽压差最高,其次为宁夏、陕西、甘肃和青海;近30 a整体上西北地区饱和水汽压差呈上升趋势,其中宁夏和新疆饱和水汽压差上升幅度最大,分别为0.036 kPa·(10a)^-1和0.033 kPa·(10a)^-1,其次为甘肃[0.026 kPa·(10a)^-1]、青海[0.021 kPa·(10a)^-1]和陕西[0.012 kPa·(10a)^-1];(2)西北各省(区),16 a尺度周期对小波方差贡献最大,为饱和水汽压差变化的主周期。此外,陕西、甘肃和新疆还存在24~27 a的周期特征,方差贡献较小;(3)相对于指数模型,ARIMA模型均方根误差平均减少42.3%,决定系数R2平均提高11.1%,Nash-Sutclife效率系数平均提高17.7%,有效提高了饱和水汽压差预测精度;(4)未来一段时间内,西北各地区饱和水汽压差均存在不同程度的升高趋势,以宁夏和新疆地区的饱和水汽压差增幅最为明显,分别为9.5%和8.9%。     

ARIMA模型在河南省PDSI旱度模式预测中的应用(英文)

[目的]构建准确度较高的旱情预测模型。[方法]采用ARIMA回归模型,对已经建立的河南省帕默尔旱度模式(PDSI)时间序列进行分析建模,借助DPS数据处理软件构建未来干旱预测模型。[结果]通过选取合适的参数,能够较好地预测未来干旱发生的可能性。[结论]ARIMA模型在PDSI的干旱的分析和预测上具有一定的实用性和较好的预测精度。