scholarly journals A time series analysis of bulk tank somatic cell counts of dairy herds located in Brazil and the United States

2017 ◽  
Vol 47 (4) ◽  
Author(s):  
Liz Gonçalves Rodrigues ◽  
Maria Helena Cosendey de Aquino ◽  
Márcio Roberto Silva ◽  
Letícia Caldas Mendonça ◽  
Juliana França Monteiro de Mendonça ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Southeastern Brazil ◽  
Dairy Herds ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
The Usa ◽  
Bulk Tank

ABSTRACT: Bulk tank somatic cell counts (BTSCC) is widely used to monitore the mammary gland health at the herd and regional level. The BTSCC time series from specific regions or countries can be used to compare the mammary gland health and estimate the trend of subclinical mastitis at the regional level. Three time series of BTSCC from dairy herds located in the USA and the Southeastern Brazil were evaluated from 1995 to 2014. Descriptive statistics and a linear regression model were used to evaluate the data of the BTSCC time series. The mean of annual geometric mean of BTSCC (AGM) and the percentage of dairy herds with a BTSCC greater than 400,000 cells mL-1 (%>400) were significantly different (P<0.05) according to the countries and the times series. Linear regression model used for the USA time series was statistically significant for AGM and the %>400 (P<0.05). The first and second USA time series presented an increasing and decreasing trend for AGM and the %>400, respectively. The linear regression model for the Brazil time series was not significant (P>0.05) for both dependent variables (AGM and %>400). The Brazil time series showed no increasing or decreasing trend for the AGM and %>400. Consequently, approximately 40 to 50% of the dairy herds from southeastern Brazil will not achieve the regulatory limits for BTSCC over the next years.

Bulk tank milk somatic cell counts in dairy herds with different bovine viral diarrhoea virus status in Poland

2014 ◽  
Vol 116 (1-2) ◽  
pp. 183-187
Author(s):  
Jolanta G. Rola ◽  
Magdalena Larska ◽  
Monika Grzeszuk ◽  
Lukasz Bocian ◽  
Aleksandra Kuta ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Bovine Viral Diarrhoea Virus ◽  
Dairy Herds ◽  
Bovine Viral Diarrhoea ◽  
Bulk Tank Milk ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Diarrhoea Virus ◽  
Bulk Tank

A hybrid modelling method for time series forecasting based on a linear regression model and deep learning

2019 ◽  
Vol 49 (8) ◽  
pp. 3002-3015 ◽  
Author(s):  
Wenquan Xu ◽  
Hui Peng ◽  
Xiaoyong Zeng ◽  
Feng Zhou ◽  
Xiaoying Tian ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Time Series Forecasting ◽  
Hybrid Modelling ◽  
Modelling Method

scholarly journals Consistency of Konker-Bassett estimators in linear regression model

2018 ◽  
pp. 17-24
Author(s):  
O. Ivanov ◽  
N. Kaptur ◽  
I. Savych
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Random Noise ◽  
Observation Time ◽  
Stationary Time Series ◽  
Model Parameters ◽  
Local Transformation ◽  
Stationary Time

Asymptotic properties of Koenker - Bassett estimators of linear regression model parameters with discrete observation time and random noise being nonlinear local transformation of Gaussian stationary time series with singular spectrum are studied. The goal of the work lies in obtaining the requirements to regression function and time series that simulates the random noise, under which the Koenker - Bassett estimators of regression model parameters are consistent. Linear regression model with discrete observation time and bounded open convex parametric set is the object of the studying. For the first time in linear regression model with described stationary time series as noise having singular spectrum, the weak consistency of unknown parameters Koenker - Bassett estimators are obtained. For getting these results complicated concepts of time series theory and time series statistics have been used, namely: local transformation of Gaussian stationary time series, stationary time series with singular spectral density, expansions by Chebyshev - Hermite polynomials of the transformed Gaussian time series values.

scholarly journals CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 147-154
Author(s):  
M. Evers ◽  
A. Thiele ◽  
H. Hammer ◽  
E. Cadario ◽  
K. Schulz ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Deformation Pattern ◽  
Mean Velocity ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
The Mean

Abstract. Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

Validation des données hydrométriques par des techniques multivariées de filtrage

1996 ◽  
Vol 23 (1) ◽  
pp. 218-230
Author(s):  
Saad Bennis ◽  
Sylvain Côté ◽  
Narut Kang
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Reservoir Level ◽  
Validation Process ◽  
Time Direction ◽  
Times Series ◽  
Before And After ◽  
River Site

The purpose of this research project was to develop a method for automatic validation of historical daily natural runoff data. Reservoir level measurements, on which natural runoff calculations are directly based were validated. Depending on the number of limnimeters installed, two different approaches were used to validate and adjust reservoir level times series. The best conditions (those discussed here) are when a reservoir has several water-level stations. Under these conditions, multivariate filtering is used to validate time series of recorded levels at each station. This method, called the multifilter method consists of comparing deviations between the value predicted with an autoregressive model, the measured historical value, and an estimate obtained using a regression model at neighbouring stations. Among the measured value and the estimate derived from the linear regression model, the closest value to the forecast was retained. One advantage in validating historical hydrometric data is the availability of data before and after the date to be validated. In other words, to validate the value of level Nt not only are the values Nt−1Nt−2, … available, but also the values Nt+1, Nt+2 … at the station to be validated as well as at neighbouring stations. To take advantage of this, the multifilter validation process was performed twice: in the usual time direction and backwards. The historical value was considered faulty and discarded only if it was rejected in both the forward and backward validation processes. All techniques developed have been incorporated into the software called ValiDeb and successfully tested at the Gatineau River site in Quebec. Key words: validation, filtering, multivariate, equipment redundance, analysis, levels, runoff, Kalman. [Journal translation]

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