# An Approach to Data Analysis in 5G Networks
## 5 Use Case Descriptors
### Forecast \(Ft\)  
The \(Forecasts=\{Ft_{1},...,Ft_{n}\}\), \(n\geq 1\) are specifications of the objects that must be projected per use case. In this way it is possible to enhance the selection of prediction algorithms and forecasting models. Given the nature of the monitoring environment, _a priori_, this approach only considers predictions on two data types: time series and graphs. The time series allow estimating the evolution of Key Performance Indicators (KPI) or thresholds from concrete locations on SELFNET (physical infrastructure, network devices, virtualization, etc.). The prediction on graphs facilitates the inference of changes on large regions of the SELFNET topology, such as spreading of congestion, inclusion of new network elements or failures. This expert system considers prediction results as facts, so \(Ft\) only refers to their specification when on-boarding new use cases. In Figure 1 predictions as facts are declared as \(Fa(Ft)\). The following expression describes the forecasts on time series:  
\[Ft_{i}:\{timeSeries\mid object\mid domain\quad lenght\} \tag{7}\]  
**Examples:**  
\(\{timeSeries\mid O_{1}\mid obs\mid t+5\}\)  
\(\{timeSeries|O_{2}\mid time\mid Today\quad 13:28:15\}\)  
where \(timeSeries\) is a reserved word indicating that the prediction is on time series, \(object\) declares the nature of the data to be analyzed, and \(domain\) is the extension of the prediction. The examples show two reserved words, \(obs\) (observations) and \(time\) (timestamp). When the time is measured in observations, the length of the prediction is indicated from the initial time instant \(t\) and the amount of coming observations (ex. \(t+5\) indicates forecast the next five observations). On the other hand, timestamps directly detail how long must be the prediction (ex. Today 13:28:15 indicates the requirement of forecast a certain object between now and 13:28:15 today). Note that the term \(timeSeries\) is used to describe the way in which data is structured and not the prediction algorithm. A record tracking of this nature could be forecasted by traditional time series methods (autoregressive moving average, exponential smoothing, extrapolation, etc.) but also by other very different approaches (drifting, naive-based algorithms, Artificial Neural Networks-ANN, Support Vector Machines-SVM, etc.). It is up to the decision component of Prediction, select the most appropriate forecasting strategy. If the prediction considers observations on graphs, the forecasts are specified as follows:  
\[Ft_{i}:\{graph\mid object\mid noVertex\mid domain\mid lenght\} \tag{8}\]  
**Examples:**  
\(\{graph\mid O_{1}\mid 30\mid obs\mid t+20\}\)  
\(\{graph\mid O_{2}\mid 45\mid timestamp\mid Today\quad 19:12:07\}\)  
\(\{graph\mid O_{3}\mid 10\mid timestamp\mid Today\quad 22:30:00\}\)  
where \(graph\) is the reserved word to declare predictions on graphs. \(object\) is the nature of the data on the edges of its incidence matrix. \(noVertex\) is the number of vertex (i.e., dimension \(noVertex\)-\(by\)-\(noVertex\) of its complete adjacency matrix). The last two parameters (\(domain\) and \(length\)) have the same function as in the expression of \(timeSeries\) prediction (indicate the measurement of time and the extension of the prediction).