## 5 Discussion and Conclusions  
Through the experiment and analysis in this paper, we find that the method proposed by [PERSON] et al. [27] is very effective in the detection of outliers and quality control of time series data in water environment monitoring. Using the key parameter of the detection method, the size of the sliding detection window, through the experiment and analysis in our work, the new method is simple and effective. After a thorough analysis of the relationship between sliding window size and detection accuracy, it can be found that there is a complex multidimensional logical correspondence between them, which makes it difficult to establish a quantitative mathematical model. In dealing with these problems, the Monte Carlo method is undoubtedly the best method and can fully take advantage of computers and statistics. Because of the advantages of the Monte Carlo method, it has been widely used in many different fields [34; 35; 36]. The Monte Carlo method also has its shortcomings, the most prominent of which is that it has random characteristics in simulation modeling, the simulation results are statistically significant, and each independent simulation result is uncertain. According to the experimental results in this paper, the accuracy and efficiency of the calculation results are different in different sampling scales and optimal window sizes. Accuracy and calculation efficiency cannot be  
Figure 5: Detection of outliers and sequence reconstruction of electronic conductivity (EC) and chlorophyll-a (Chl-a) time series by optimal window size sliding. **(a-1)**: The original observation time series data and reconstruction time series data of EC; **(a-2)**: Local curve of red box in the figure **(a-1)**; **(b-1)**: The original observation time series data and reconstruction time series data of Chl-a; **(b-2)**: Local curve of the red box in the figure **(b-1)**. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{3}{*}{**Water Factors**} & \multicolumn{2}{c}{**Optimal Window Sizes**} & \multicolumn{2}{c}{**Suboptimal Window Sizes**} & \multicolumn{2}{c}{**Worst Window Sizes**} \\ \cline{2-7}  & **Window** & **Outliers** & **Window** & **Outliers** & **Window** & **Outliers** \\  & **Size** & **Rate (\%)** & **Size** & **Rate (\%)** & **Size** & **Detection** \\ \hline
**pH** & 14 & 5.25 & 12 & 5.23 & 23 & 3.01 \\ \hline
**Dissolved Oxygen** & 12,14 & 3.09 & 11 & 2.99 & 29 & 1.80 \\ \hline
**Electronic Conductivity** & 16 & 6.69 & 15 & 6.58 & 5 & 3.47 \\ \hline
**Turbidity** & 16, 17, 18 & 5.99 & 15 & 5.95 & 7 & 3.56 \\ \hline
**Chlorophyll-a** & 17 & 3.05 & 16 & 3.02 & 6 & 2.10 \\ \hline
**Algal Density** & 14 & 3.22 & 16 & 2.90 & 7 & 2.10 \\ \hline
**Permanangate Index** & 9 & 6.23 & 12 & 5.81 & 30 & 4.04 \\ \hline
**Ammonia-Nitrogen** & 8 & 12.47 & 14 & 12.13 & 28 & 6.66 \\ \hline
**Total Phosphorus** & 12 & 7.16 & 14 & 6.99 & 5 & 4.04 \\ \hline
**Total Nitrogen** & 14 & 7.92 & 9 & 7.58 & 30 & 4.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison information of different representative window sizes and anomaly detection rates. * _[PERSON] and Chapter 1 -Methods for Analysis of Trends and Changes in Hydroclimatological Time-Series. In _Trends and Changes in Hydroclimatic Variables_; [PERSON]; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1-89. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] An algorithm for the difference correlation filter for multi-parameter marine timing observation data. _Acta Oceanolgica Sin._**2012**, _34_, 51-58. * _[PERSON] (1994)_ [PERSON]; [PERSON] The Kalman filter model and Bayesian outlier detection for time series analysis of BOD data. _Ecol. Model._**1994**, _73_, 159-165. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Generalised linear model-based algorithm for detection of outliers in environmental data and comparison with semi-parametric outlier detection methods. _Atmos. Pollut. Res._**2019**, _10_, 1015-1023. [CrossRef]
* _[PERSON] (1972)_ [PERSON] Outliers in Time Series. _J.