# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 2 Link and End-to-End Quality Prediction in Heterogeneous Networks
Their main contributions are: (1) the use of time-series (TS) analysis to estimate LQ in the routing layer for real-world WMCN; (2) the detailed evaluation of results, assuming several learning algorithms to show the potentiality of TS analysis for estimating LQ in short and long term; and (3) the evidence that LQ computed from TS can be used to accurately predict future values in WMCN. In the same way, [PERSON] and [PERSON] [43] proposed applying an online learning algorithm that progressively updates the model, and to perform evaluations in a mobile environment, with more variability than the stationary network used in [20]. There are two different approaches for applying prediction to routing [21; 22]: (1) new routing algorithms based on Machine Learning and Deep Learning; and (2) classical routing algorithms but with Machine Learning based metrics. These two approaches are widely used in other environments, for example Software-Defined Networking (SDN). In this work, we focus on the latter approach. Several authors deal with the Machine Learning and Deep Learning Based Routing approach. For instance, [PERSON] et al. [44], proposed NeuRoute as a dynamic routing framework. It uses traffic in real time to learn traffic characteristics and generates forwarding rules, the routing tables. In [45], the authors presented an intelligent routing protocol based on reinforcement learning to choose the best data transmission paths according to the and network status. A QoS-aware Adaptive Routing (QAR) method is presented in [46] to enable adaptive packet forwarding by applying reinforcement learning. The routing path with the maximum QoS-aware reward function is selected. The key issue is that there are important differences between Wireless Mesh Community Networks and SDN. SDN has no computational constraints (powerful servers) or network constraints (very high speed links). In addition to those constraints, community networks have a great heterogeneity at all levels: links, routers and wireless medium. Therefore, nowadays it is not worth deploying these proposals in Mesh Community Networks. On the other hand, some other authors assume classical routing algorithms but with Machine Learning based on metrics. For instance, [PERSON] and [PERSON] [47] proposed NeuTM that predicts traffic matrix in real time using a Long Short-Term Memory (LSTM) framework. Unfortunately, when links are heterogeneous, the traffic matrix does not indicates if a link or path is overloaded or how much additional traffic it could handle. Therefore, in very heterogeneous environments, such as Wireless Mesh Community Networks, it is better to focus on predicting service quality metrics, as we do in this work. [PERSON] et al. [48] also extended their own work [20] to demonstrate that it is possible to accurately predict LQ in 98% of instances (in both the short and long term). The behavior of the links is globally analyzed to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, the behavior of links individually is also analyzed to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Moreover, the work also presents an optimized prediction method that considers the knowledge about the expected LQ values (saturation). [PERSON] et al. [23] identified some limitations and problems of predictive models generated using batch machine learning algorithms in [48]: they cannot be updated once their training has been completed; they impose a predeployment effort (a set of quality samples must be collected in each link before building the corresponding prediction models); and predictions are not available until the observation period is over (which might take a long time). To address these problems, they propose a new hybrid online algorithm for LQ prediction.