# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 4 Time-Series Analysis to Predict EtEQ  
### Experimental Framework  
As stated before, we dealt with time-series analysis to estimate LQ in the routing layer for real-world WMCNs. In this way, the value of the ETX EtEQ prediction of a path can be used as a selection metric by the routing algorithm. OLSR uses the aggregated ETX of each path, selecting the lowest aggregated ETX value. This aggregate value is computed using the measured ETX values in all the links and propagated to the rest of the network. In the case of large networks, this propagation takes a considerable time. As shown in Figure 1, the full propagation could take up to 80 s. In addition, the ETX value of a link is computed based on an active proving history. Therefore, the ETX of a link takes time to show that a link is overloaded. With the ETX EtEQ prediction, we are estimating future values. In this way, the routing algorithm could react to changes in the network, e.g., an overloaded link. To do this, we assume the FunkFeuer experimental network and an OLSR dataset of several nodes and links. A time series is a set of data collected over time with a natural temporal ordering. It differs from typical Data Mining or Machine Learning applications, where the ordering of data points within a dataset is not important. Time-series analysis is the process of using statistical techniques to model and  
Figure 8: Histogram of deviation between average ETX and number of hops. Figure 7: Deviation between average ETX and hops normalized by the corresponding number of hops. explain a time-dependent series of data points. Similarly, time-series forecasting is a method that uses a model to generate predictions (forecasts) of future events based on known past events. In our case, we used more than one prediction algorithm, so that we do not rely on a specific learning technique. We applied four of the best well-known approaches [51]: Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Regression Trees (RT), and Rule-Based Regression (RBR). * The Support Vector Machine (SVM) algorithm has recently become one of the most popular and widely used methods in Machine Learning. It performs a linear or nonlinear division of the input space, and builds a prediction model that assigns target values into one or another category. * The k-Nearest Neighbors (kNN) algorithm is one of the most simple Machine Learning algorithms as it makes no assumptions on the underlying data distribution. This algorithm takes the \(k\) data-points closest to the target value, and picks the most common one. * Regression Tree (RT) is a type of decision-tree algorithm where the target value can have continuous values. This method recursively partitions the data space and runs a simple prediction model within each partition. * Finally, the Rule-Based Regression (RBR) algorithm is similar to a decision-tree approach, but it is a stronger model that provides rules that are often potentially more predictive. We applied training and test-sets validation to assess the predictive accuracy of the models. After a model is processed using the training set, it is tested by making predictions against the test set. Although the algorithms need some days to start making predictions after the appearance of a new path, this work assumes scenarios where nodes and links do not appear very often, as shown in Figure 2. In any case, when there is no room for prediction, the actual ETX is assumed. For this purpose, we used the Weka-workbench system [52], a framework that incorporates a variety of learning algorithms, and some tools for the evaluation and comparison of the results. Weka has a dedicated environment for time-series analysis that allows forecasting models to be developed and evaluated. The Weka's time-series framework takes a Machine Learning or Data Mining approach to model time-series by transforming the data into a form that can be processed by standard propositional learning algorithms.