This study detects the presence of seasonality, stationarity, and long-range memory structures in daily radon measurements from a permanent monitoring station in central Italy. The transient dynamics and the seasonality structure are identified by power spectral analysis based on the continuous wavelet transformation and a clear 1-year periodicity emerges. The stationarity in the data is assessed with the Dickey–Fuller test; the decay of the estimated autocorrelation function and the estimated Hurst exponent indicate the presence of long-range dependence. All the main characteristics of the data have been properly included in a modeling structure. In particular, an autoregressive fractionally integrated moving average (ARFIMA) model is estimated and compared with the classical ARMA and ARIMA models in terms of goodness of fit and, secondarily, of forecast evaluation. An autoregressive model with a noninteger value of the differencing parameter (d=0.278) resulted to be the most appropriate on the basis of the Akaike Information Criterion, the diagnostic on the residuals, and the root mean squared error. The results suggest that there is statistically significant evidence for not rejecting the presence of long memory in the radon concentration. The radon measurements are better characterized as being stationary, but with long memory and so, the statistical dependence decays more slowly than an exponential decay.

这项研究从意大利中部一个常设监测站的日常detects测量中检测出季节性，平稳性和远程记忆结构的存在。通过基于连续小波变换的功率谱分析来识别瞬态动力学和季节性结构，并出现清晰的1年周期。数据的平稳性通过Dickey-Fuller检验进行评估；估计的自相关函数的衰减和估计的Hurst指数表明存在长期依赖性。数据的所有主要特征均已正确包含在建模结构中。特别是，估计了自回归分数积分移动平均值（ARFIMA）模型，并将其与经典ARMA和ARIMA模型进行了拟合优度比较，其次，预测评估。差分参数为非整数值的自回归模型（d=0.278）在Akaike信息准则，残差诊断和均方根误差的基础上得出最合适的结果。结果表明，有统计学意义的证据表明不拒绝concentration浓度中长记忆的存在。better测量的特征较好是静止的，但具有较长的记忆，因此，统计依赖性的衰减比指数衰减更慢。