Two geostatistical techniques, namely, moving average and kriging, were adopted.
In the present study, two interpolation techniques, namely, moving average and ordinary kriging, have been evaluated to estimate the soil moisture at spatial scale.
Namely, the moving average smoothing is used to reduce the errors introduced by the reconstruction when the number of Hermite coefficients is significantly lower than the number of the original coefficients, such as Figure 4 (a) Original sequence with 360 DC components, (b) the sequence reconstructed using 240 Hermite coefficients, (c) the sequence reconstructed using 180 Hermite coefficients.
Cossette et al. [1] used two integer-valued time series, namely the Poisson moving average (MA) and Poisson autoregressive (AR) processes, to model the claim frequency in the risk model.
Three single predictors, namely the autoregressive integrated moving average (ARIMA), Kalman filter (KF) and back propagation neural network (BPNN) are designed and incorporated linearly into the BCM to take advantage of each method.
Compared with the conventional least-squares-residuals method, the proposed filter type method, namely the exponentially weighted moving average (EWMA) filter, can achieve the goal of fast satellite failure detection in receiver autonomous integrity monitoring (RAIM).
The performance of these two models was also compared to that of a conventional forecasting method, namely the auto regressive integrated moving average with exogenous input (ARIMAX) model.
The filter is then compared with two candidate de-noising techniques, namely: i) a Wiener causal filter introduced by Su et al. (2013) and ii) a conventional moving average filter.
Now a four-point moving average, and next the five-point moving average, and a six-point moving average next.
Auto-regressive, moving average and ARIMA models.
