Abstract
This study sets out an empirical hybrid autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model designed to estimate electromagnetic wave propagation in densely forested urban areas. Received signal power intensity data was acquired through measurement campaigns carried out in the Metropolitan Area of Belém (MAB), in the Brazilian Amazon. Comparisons were made between estimates from classical least squares (LS) fitting and ITU (International Telecommunication Union) recommendation P. 1546-5. The results indicate the model is, at least, 44% more precise than every ITU estimate and, in some situations, is at least 11% better than an LS estimate, depending on the respective values of the relative error (RE).
Highlights
Introduction is study examines a hybrid autoregressive integrated moving average (ARIMA)-artificial neural network (ANN) model inspired by [1] a model to predict received signal power intensity at a receiver (Rx) location as a function of the distance to the transmitter (Tx). is study is based on the Brazilian digital television (DTV) frequency range and looks at the special case of a densely forested and urbanized city in the Amazon region
In [5], a hybrid ARIMA-ANN is proposed to predict the incidence of hepatitis in Heng County, China. e results were compared with the single ARIMA and single ANN estimates. e authors of [6] propose a hybrid SARIMA (Seasonal ARIMA) and nonlinear autoregressive neural network (NARNN) for forecasting the incidence of hand-foot-and-mouth disease in Chenzen, China
It seems to be unnecessary to tackle this specific problem by complementing it with the ANN to fit the non-linear terms of the studied series, since when the network is applied, there is a slight increase in errors
Summary
Time series is a sequence of observations taken sequentially in time [12], or, in other words, an outcome of a stochastic process. We use the ARIMA model to make a first adjustment on the analysed series to represent its linear information. There is an interpolation branch in the testing process We did this for two reasons: first, to increase the number of samples for each measured dataset, which allows the ARIMA model to work with more samples and, refine its adjustments. The interpolated group of datasets is able to simulate a “no stop” measurement campaign scenario, which is usually more desirable than a “stop-and-go” campaign scenario, where it is necessary to stop at every measured point to acquire data. After ensuring that the analysed series is stationary, we proceed to an analysis of ACF and PACF When these functions behave like that of a stationary process, we can define the order of the ARIMA model [12]. When analysing other datasets or fitting this modelling on another problem, these steps may become mandatory
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