# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 6 Conclusions  
This study demonstrated that time-series analysis is a promising approach to accurately predict EtEQ values in Community Networks. This technique can be used to improve the performance of the routing protocol, by providing information to make appropriate and timely decisions, to maximize the delivery rate and minimize traffic congestion. The dataset that we used in our analysis shows quite significant fluctuations (about 7%) in the temporal evolution of the number of paths (routing entries). This contrasts with the general assumption in mesh networks that the number of paths is stable. The persistence of paths in our dataset is very stable for 90% of paths, but 10% of paths have persistence values below 60%, and those paths create the temporal fluctuations in the number of paths. Our study includes paths with persistence above 25%. Regarding the Expected Transmission Count (ETX) behavior, the average ETX Path Quality is more stable at night, and presents more fluctuations during the morning. On non-working days, this behavior is less apparent. We assume this is caused by the variation of network traffic and interference during the day, leading to packet loss. Hence, the accuracy of prediction depends on the day and time when it is applied. On the other side, the most frequent number of Hops and ETX values are in the range of 5-10. The dispersion of ETX according to the number of Hops is minimal for 1-4 Hops and more than 14 Hops. However, the dispersion is significant for 5-9 Hops, and relevant for 10-13 Hops. Therefore, the accuracy of the predictions would be better or worse depending on the number of Hops. We present results from four well known learning algorithms that model time series: Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Regression Tree (RT), and Rule-Based Regression  
Figure 23: Histogram of end-to-end delay. Figure 22: Histogram of the ETX of links. (RBR). All of them achieved high percentages of success, with average Mean Absolute Error values per link between 2.4% and 5% when predicting the next value of the EtEQ. We also analyzed the error variability and found that three of the learning algorithms presented similar performance (RT, RBR, and SVM), whereas kNN performed worse due to outliers with larger errors. A more detailed study of RBR prediction showed an average absolute-error less than 1. We also observed differences in the prediction behavior during day and during night, as happened with actual ETX values. Additionally, we also measured the computational cost of the prediction concluding that the proposed methodology does not consume significant amount of resources and that the impact in a router system is very low in terms of CPU utilization, energy consumption and temperature. Finally, the analysis of a use case allowed us to compare two routing strategies. The results show that our approach behaved equal to or better than ETX + Dijkstra in terms of Packet Delivery Ratio and Link ETX. However, the End-to-End Delay metric presented slightly worse results. As future work, we want to extend this analysis to other Community Networks [8] to evaluate if the observed behavior could be generalized. Moreover, we plan to identify which Paths contribute most to the error in the EtEQ prediction and to understand what factors make it more difficult to predict the behavior of these paths. We also want to study the impact of errors in routing decisions, and to explore how accuracy could be increased by deploying two different predictors, for day and night. Finally, we plan to improve the prediction process by discarding those paths whose relation between EtEQ and prediction accuracy is above a certain threshold. This discarding can be done at the very beginning (static discarding) or at any given time (dynamic discarding). Conceptualization P.M., C.M. and R.M. ; Methodology P.M. and R.M. ; Software P.M. and C.A. ; Vialidation P.M. and C.A. ; Investigation P.M. and C.A. ; Writing--Original Draft Preparation P.M., E.D. and C.M. ; Writing--Review & Editing P.M., C.A. and E.D.