ライフサイエンスコーパス: moving average

1 oughout the study period based on the 5-year moving average.

2 ssion and seasonal autoregressive integrated moving average.

3 moment-to-moment ISCs computed using a 17-s moving average.

4 of incidence data, which typically focus on moving averages.

5 fall and temperature, and calculated 10-week moving averages.

6 regressive moving-average models with 2-year moving averages.

7 n index and augmentation pressure at several moving averages.

8 ed statistical significance for 3- and 4-day moving averages.

9 BC moving averages (5-day to 28-day) were associated with i

10 stroke differed by device indication (simple moving averages: AF management, <8 days and suspected AF

11 ed time-varying contextual variables and 3-y moving-average ambient PM2.5 at a 1 x 1 km spatial resol

12 lysis and T-wave alternans (TWA) by modified moving average analysis.

13 was assessed from digitized ECGs by modified moving average analysis.

14 ch as support vector machine, autoregressive moving average and artificial neural network (ANN).

15 Learning curves were plotted using weighted moving average and CUSUM analysis was used to determine

16 evalence over time by calculating the 5-year moving average and fitting restricted cubic spline model

17 TWA was examined by the modified moving average and intrabeat average analyses.

18 s forecasting via auto regressive integrated moving average and machine learning methods to simulate

19 e threshold-modelled predictors for 2-7-days moving averages and for patients from specific ABO blood

20 th higher levels of BNP beginning with 2-day moving averages and reached statistical significance for

21 sed causal associations of long-term (1-year moving average) and short-term (2-day moving average) ex

22 ewhart control chart, exponentially weighted moving average, and cumulative sum charts provide a mean

23 Daily AF burden was transformed into simple moving averages, and temporal AF burden trends were defi

24 on of colic with certain covariates, using a moving average approach to conditional logistic regressi

25 s along the Atlantic coast of Europe using a moving-averaged approach based on coastline characterist

26 sion (Prophet) and Autoregressive Integrated Moving Average (ARIMA & Auto-ARIMA) methods.

27 al Networks (ANN), Autoregressive Integrated Moving Average (ARIMA) and hybrid models (CNN + LSTM and

28 hniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transf

29 study also used an autoregressive integrated moving average (ARIMA) model to forecast expected Google

30 tor scalar, and an autoregressive integrated moving average (ARIMA) model.

31 ime-series design, autoregressive integrated moving average (ARIMA) models with a transfer function w

32 ine learning-based autoregressive integrated moving average (ARIMA) models with the social distancing

33 tion structure and autoregressive integrated moving average (ARIMA) models, magnitude, and age distri

34 Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead f

35 Using automated autoregressive integrated moving average (ARIMA) time series outlier detection mod

36 Autoregressive moving average (ARMA) models accounted for autocorrelati

37 ing is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics.

38 TWA magnitude was measured by modified moving average beat analysis, and the complexity of T-wa

39 s (from 35 to >/= 85 years) using three year moving averages between 1982 and 2006.

40 ewhart control chart, exponentially weighted moving average chart, and cumulative sum chart.

41 found a nonlinear association between 12-mo moving average concentration of smoke PM(2.5) and monthl

42 In Poisson generalized linear models, 3-day moving average concentrations of ozone, nitrogen dioxide

43 An interquartile-range increase of the moving average concentrations of same-day and previous-d

44 Chicago air, examining plots of the 365-day moving average concentrations shows that they do not dec

45 Our results show that linear-weighted moving average consistently outperforms the other peak p

46 os, the risk-adjusted Exponentially Weighted Moving Average control chart had the shortest median tim

47 The risk-adjusted Exponentially Weighted Moving Average control chart signaled the fastest in mos

48 for the risk-adjusted Exponentially Weighted Moving Average control chart.

49 ime-updated 12 month lagged, 24 month simple moving average cumulative exposure, increased risk of lu

50 A regression with autoregressive moving average errors model was employed to adjust for s

51 The exponentially weighted moving average (EWMA) control chart is effective in dete

52 ion variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling.

53 oring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme.

54 1-year moving average) and short-term (2-day moving average) exposure to particulate matter with an a

55 calculated as the average of all past 2-year moving average exposures across person-years, was 0.51 m

56 onfidence interval: 1.11, 1.15) for 12-month moving average exposures.

57 e by using fixed monitors, and we determined moving averages for 1-7 days preceding vascular testing.

58 e generated using 7-, 14-, and 21-day simple moving averages for every team and were plotted against

59 Risk ratios generally increased with longer moving averages; for example, an elevation in 60-month m

60 recasting methods (Autoregressive Integrated Moving Average, Holt-Winters, gradient-boosting machines

61 including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipel

62 We assessed associations between the 3-day moving average (lag 0-1-2) of Betulaceae (except Alnus s

63 daily counts of ED visits and either the 3-d moving average (lag 0-2) of OP(DTT) or same-day OP(DTT).

64 cs, for individual lag days 0-6, and for 3-d moving averages (lag 0-2).

65 d status regardless of specific pollutant or moving average lags.

66 Our approach is to consider the set of moving-average (linear) processes and study its closure

67 Neighboring probes are combined through a moving average method (MA) or a hidden Markov model (HMM

68 Spectral Method and the time-domain Modified Moving Average method have demonstrated the utility of T

69 ocardiogram-based TWA analysis with Modified Moving Average method have yielded significant predictiv

70 A and TWA were measured by enhanced modified moving average method.

71 model (auto) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortali

72 ormed the baseline autoregressive integrated moving average model across all metrics.

73 After fitting an autoregressive integrative moving average model and taking the residuals, all pairw

74 lysis based on the autoregressive integrated moving average model to compare changes in age-adjusted

75 ysis and a generalized linear autoregressive moving average model to examine avalanche-climate relati

76 tivariate seasonal autoregressive integrated moving average model was developed to track influenza ep

77 analyzed using the autoregressive integrated moving average model.

78 orrelation component using an autoregressive moving-average model, and (3) a linear predictor to acco

79 a SARIMA (seasonal autoregressive integrated moving average) model, a common method to forecast injur

80 e SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches t

81 al analysis was by autoregressive integrated moving average modeling.

82 e Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per

83 based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of

84 Autoregressive integrated moving average models and generalized estimating equatio

85 and used segmented autoregressive integrated moving average models for the analysis.

86 osure to PM(10) and risk of ACS, with 7-days moving average models stratified by blood group revealin

87 sed interventional autoregressive integrated moving average models to examine the impact of ZVL marke

88 Autoregressive integrated moving average models were calculated.

89 eries analysis and autoregressive integrated moving average models were used to evaluate changes in s

90 Interventional autoregressive integrated moving average models were used to examine the associati

91 Autoregressive integrated moving average models were used to project spending to 2

92 Autoregressive integrated moving average models with step and ramp intervention fu

93 so evaluated using autoregressive integrated moving average models, adjusting for temporal autocorrel

94 Separately, using autoregressive integrated moving average models, we estimated total, per-capita, a

95 by use of seasonal autoregressive integrated moving average models.

96 y 2020 fitted with autoregressive integrated moving average models.

97 ion using seasonal autoregressive integrated moving average models.

98 Auto-regressive integrated moving-average models were used to calculate projected r

99 yzed trends using generalized autoregressive moving-average models with 2-year moving averages.

100 ode decomposition detrending and (2) a novel Moving Average (MVG) detrending approach.

101 quartile range increase (2.5 ng/m3) in 7-day moving-average Ni was associated with 2.48-mmHg (95% CI:

102 ormance of embedded digital filters, namely, moving average, numerically simulated low pass RC, and G

103 hou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382.

104 hat when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there wa

105 an, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chan

106 When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there wa

107 A 5 degrees C change in the 4-day moving average of apparent temperature was associated wi

108 er, slope, distances to larger cities, and a moving average of current population, were locally adapt

109 ature and a rise of 10 mug/m(3) in the 3-day moving average of mean daily PM(2.5) were associated wit

110 studies, a rise of 10 degrees C in the 2-day moving average of mean daily temperature and a rise of 1

111 ry breadth was low coverage if the 4-quarter moving average of MS DMT drug or class coverage was belo

112 per 100,000 people per month when the 12-mo moving average of PM(2.5) concentration was of 0.1 to 5

113 The 7-day moving average of positive cases was inversely associate

114 A 3-year moving average of residential exposures to selected poll

115 We use a moving average of retained and non-retained genes to fin

116 Country-years were grouped by 10-year moving average of routine measles vaccination coverage (

117 nfounders, a 3.4-mug/m(3) increment in 2-day moving average of same-day and previous-day nitrate conc

118 he predictive model suggested that the 3-day moving average of sporadic cases was positively associat

119 We report a 3-week moving average of the number of rotavirus tests, positiv

120 activity was defined by comparing the 4-week moving average of the positivity rate to its annual aver

121 he strongest associations were observed with moving averages of 2-7 days after a lag of several days.

122 regression model as continuous functions of moving averages of air pollution exposures (over 4 hours

123 Six-day moving averages of all 4 particle metrics were associate

124 al incidence rates from 2002-2011 and lagged moving averages of annual estimates for PM2.5 were also

125 A 5 degrees C change in 3- and 4-day moving averages of apparent temperature was associated w

126 We investigated the effects of moving averages of black carbon of 1-5 years before the

127 nd was defined as the comparison of 2 simple moving averages of different periods for each diagnostic

128 Moving averages of mean daily PM(2.5) and temperature we

129 ntricular arrhythmias (VA) with 0- to 21-day moving averages of PM(2.5) and particle radioactivity (2

130 The 1- and 2-day moving averages of PM2.5, NO2, and O3 before testing wer

131 e also examined linear relationships between moving averages of pollutant concentrations 1, 2, 3, 5,

132 mparison of pp32 with the pp32r1 sequence by moving averages of sequence identity reveals divergence

133 d as the number of active clinicians, 3-year moving averages of weekly work hours by individual physi

134 The estimated effect of 28-day BC moving average on systolic BP was 1.95-fold larger for i

135 ssive symptoms, and significantly for 30-day moving average (OR = 1.16; 95% CI: 1.05, 1.29) upon SES

136 toms, with the largest increase for 180-days moving average (OR = 1.61; 95% CI: 1.35, 1.92) after adj

137 ial proportional odds models, both CMV 7-day moving average (OR, 5.1; 95% CI, 2.9-9.1; P < .001) and

138 stic parameters were transformed into simple moving averages over different periods for daily follow-

139 e defined as the comparison of unique simple moving average pairs.

140 hat interquartile range increases in 24-hour moving average particulate matter less than 2.5 mum in a

141 nificantly positive associations of 12-month moving average PM2.5 exposures (per 10-mug/m3 increase)

142 rages; for example, an elevation in 60-month moving average PM2.5 exposures was linked to 1.33 times

143 more conventional autoregressive integrated moving average prediction model, at all input/output num

144 Autoregressive integrated moving average regression was applied, and prevalence, w

145 ss the independent associations of 24-months moving average residential PM2.5 exposure and physical a

146 Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus

147 Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern am

148 Seasonal Autoregressive Integrated Moving Average (SARIMA), Multi-Layer Perceptron neural n

149 sis using seasonal autoregressive integrated moving-average (SARIMA) models.

150 th low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP.

151 Non-Gaussian stationary autoregressive moving average sequences are considered.

152 poses a nonparametric exponentially weighted moving average signed-rank (EWMA-SR) and also proposed a

153 ) and also proposed a homogeneously weighted moving average Signed-Rank (HWMA-SR) control charts for

154 double error shrinkage (DES) method by using moving average statistics based on local-pooled error es

155 In the standard arm, the 2-week moving average systolic BP did not change significantly

156 sequences by a time-dependent autoregressive moving average (TD-ARMA) process.

157 bias variability was refined by employing a moving average technique.

158 Seasonal autoregressive integrated moving average time series models were fitted to the pre

159 ery standard deviation increase in 28-day BC moving average was associated with 0.12 standard deviati

160 standard deviation increase in the 28-day BC moving average was associated with 1.97 mm Hg (95% confi

161 crease in particle radioactivity for a 2-day moving average was associated with 13% higher odds of a

162 ncrease in daily PM(2.5) levels for a 21-day moving average was associated with 39% higher odds of a

163 A 3-year, centered, moving average, which was used to display time trends in

164 (TF)-binding sites measured within a 200-bp moving average window through phylogenetically conserved

165 theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input - NARMAX) to assess

166 tified by a NARMAX (nonlinear autoregression moving average with exogenous input) representation fami

167 arbon, and PM2.5 mass concentrations (4-week moving average) with DNA methylation [expressed as the p

168 the Boston, Massachusetts, area (1- to 4-day moving averages) would be associated with higher levels