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ARIMA modelling comprising model investigation, estimation and diagnostic checking followed the methods of Box et al. 28 This modelling enables investigation of the size and statistical significance of changes in an outcome after a specified time point, adjusting for background trends and confounders.
We used ARIMA modelling for this study because, before testing for the impact of hypothesised interruptions-in-time, the ARIMA modelling technique enabled us to fit a statistical model to the time series that accurately predicted the monthly number of suicides that occurred in Greece over the time series.
Two main parts are included in this method: the linear clustering preprocessing part and the optimal ARIMA modelling and forecasting part.
Optimal ARIMA modelling and forecasting was conducted for each 220 kV substation load in Fig. 2b, and the results are shown in Fig. 4.
Statistics Canada X-11-ARIMA (Dagum 1980) has introduced autoregressive integrated moving average (ARIMA) modelling in particular to extend the series in the past and in the future in order to avoid end-adjustments in the moving averages.
With the success of ARIMA modelling at the end of the 20th century, methods with better outlier detection and trading day corrections by regression with ARIMA errors have appeared, with the regARIMA module of Census X-12-ARIMA or Bank of Spain TRAMO-SEATS.
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The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed.
To generate Diarrheal Remedy Sales signals we employed ARIMA modeling and forecasting.
Time-series models including autoregressive integrated moving average (ARIMA) model and seasonal ARIMA model were mainly used in recently published studies.
We accounted for the autocorrelation of residuals through autoregressive integrated moving average (ARIMA) modeling, using regression models with ARIMA errors.
ARIMA models only use the historical data.
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