Sentence examples for moving average forecast from inspiring English sources

The moving average forecast methods (Methods 2 4) performed relatively well on all data sets, showing relatively high DP and less optimistic bias (no boldface entries in the last row of Table 1) when comparing DPs from the real data or from the hierarchical model to DPs from data simulated from the non-hierarchical model.

On the other hand, moving average methods suffer when forecast errors from later stages of multi-day outbreaks are reduced once the early-stages of the outbreak increase the moving average forecast, and if there are strong day-of-week effects that are not accommodated.

The objective of this paper is to assess the behavior of moving average forecast-based control charts on data having correlation that is persistent over very long time horizons, i.e., long-range dependent.

This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment.

Few studies have used the autoregressive integrated moving average (ARIMA) model to forecast injury mortality.

In four of the seven sites, exponential smoothing was the best forecasting model, whereas in the remaining sites, moving average models provided the best forecast.

This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously.

In addition, the new Outlier Correction Method is proposed to guarantee the robustness of the built Auto Regressive Moving Average and Extreme Learning Machine models during their forecasting computation.

Employing ARIMA or autoregressive moving average (ARMA) models for time series forecasts has become increasingly popular, but the major limitation with their use is the pre-assumed linearity of the models [ 15], that often leads to combining them with other statistical techniques [ 12].

Using a disease surveillance system, one is able to apply statistical methods, such as cumulative sum (CuSum) [ 9, 10] or autoregressive integrated moving average (ARIMA) [ 11- 15], in order to forecast an epidemic curve or to detect aberrations in disease spread.

HW model is a more sophisticated method of forecasting than methods of moving average and exponential smoothing.