Partial Autocorrelation Functions of Water Level in Flood Modelling

Table 1 shows the statistical parameters of the calibration and verification data. The maximum value of the calibration period is larger than the maximum value of the verification range, and the minimum value is smaller than the maximum value of the verification. Therefore, there may not be extrapolation problems in this data set. There is no big difference in the distortion between the calibration data set and the validation data set. As shown in Figure 2, at the prediction station there is a confidence limit corresponding to the autocorrelation function and the partial autocorrelation function of the water level, with an estimated maximum of 20 lags.

Based on autocorrelation function and partial correlation function, the annual report of video game sales and worse results is sufficient for ARIMA (2,1,0) model and ARIMA (1,1,0) model to satisfy annual murder rate, 0) model (the others of the ARIMA model). For details, see Box et al. , 2008 for more information. The Box Q test of Ljung-to-white noise residuals shows that when this mode is applied to selling video games (Ljung-Box Q lags 10 5.4 5.43, P ¢. 86), the attack rate becomes severe (Ljung- "Box Q 10 ············································································ · · · · · · · · · · · · · · · · · · (10 ± 7.59, L hom box Q, P? As shown in Figure 2, annual video game sales of violence is irrelevant to the cost of co-occurrence.

Leng (2006a) studied whether the attributes of the inclusive ratio series (indicators of underwriting profitability in damages liability insurance) change over time. Using autocorrelation function (ACF) and partial autocorrelation function (PACF), the author verifies whether the combination ratio is fixed. Underwriting profit has deteriorated in recent years, and the overall cost rate is unstable. This feature of the composite ratio requires further analysis of the impact on the underwriting cycle. Conventional concepts of underwriting cycles, such as predictable cycle lengths and trends, may have changed. It identifies the possibility of a series of unsteady mixture ratios and introduces the possibility of existence of nonstationary and complex ratio interrupts

Figure 1 shows the time series of KIPSP and its autocorrelation function at monthly and quarterly summarization level. The vertical dashed line in each figure represents 9/11. Visual inspection of these sequences revealed two direct attributes: (1) the sequence is periodic (around the AR (1-4) process depending on the level of the set) and (2) The variance of the sequence increases with time. . The latter feature is particularly evident with quarterly data. The column on the right side of Fig. 1 is an autocorrelation function of three sequences and the time scale of these ACFs is based on the periodicity of the data, so "1" on the x axis is 12 months for monthly data, quarterly It is 4 pieces with data. Quarterly y axis is autocorrelation