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To control for secular trends we used an interrupted-time series with adjustment for autocorrelation using an autoregressive integrated moving average (ARIMA) model as described elsewhere [ 26].
In the second step, the remaining data is used to estimate autoregressive fractional integrated moving average (ARFIMA) statistical models describing variability of the tested WSN parameters.
The general consensus from these studies is that a 2- or 3-day moving average of PM better describes the relationship between PM and mortality than does a single day's PM (Schwartz 2000).
Notice also that in the EWMA algorithm described above, the moving average EWMA t) is always updated even if it exceeds the detection threshold UCL.
In Table 1, the TWA detection accuracy for our method was further compared with two well-known T-wave analysis methods (spectral methods (SM) [27] and modified moving average (MMA) [26], and two previously described wavelet-based methods (Wavelet 1 [29] and Wavelet 2 [30]).
Given that a 2-day moving average of air pollutants has been described as fitting better than any single day's results [ 9, 10], a model with concurrent and previous day average (lag01) was fitted.
Based on a theoretical analysis of the system dynamics, a three-order controlled auto-regressive moving average (CARMA) model is used to describe the practical pressure load systems.
Instead of a complex first-principles model, a polynomial auto-regressive moving average model (ARMA) is used to describe the nonlinear behavior of the polymerization reactor.
In this paper, the high priority traffic is described by an autoregressive integrated moving average (ARIMA) process.
An 11-year moving average was also applied to this data as described above.
The nonlinear auto-regressive and moving average model with exogenous input (NARMAX) is utilized to describe the behavior of DSSH based on the input space expansion.
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