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Gray and red lines indicate the daily optimum process noise value and 11-day moving average time series, respectively.
It is clear that the obtained time series did not stabilize during the year, and shows a clear annual pattern in the moving average time series.
Furthermore, the moving average time series obtained at station 0098 shows stability throughout the year (Fig. 7) with small annual pattern.
This paper obtains a uniform reduction principle for the empirical process of a stationary moving average time series {Xt} with long memory and independent and identically distributed innovations belonging to the domain of attraction of symmetric α-stable laws, 1<α<2.
These subjectively chosen distributions were combined with a moving average time series process for the error in the TFR or life expectancy increase.
Trends were analysed using autoregressive moving average time series and mixed effects linear regression models adjusting for all available potential confounders.
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The PV generation is predicted using an auto regressive moving average (ARMA) time series model.
The vibration signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series.
Calculate daily or n-day (i.e., 7, 30, 60) moving average from time series data.
The probability distribution model of wind power forecasting errors is determined by autoregressive moving average (ARMA) time series model.
The NARMA model is implemented by including the cross terms of output signals to a linear autoregressive moving average (LARMA) time series model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com