Application of functional feature extraction to the compression of network time series

Network management actions require the retention of data representing the temporal evolution of network state, mainly in the form of time series. Nonetheless, storing and exploiting those measurements is becoming a challenge as the production rate of such data is continuously increasing and data lasting for long time periods are used. To scale up the storage and improve both the analysis and visualization of network measurements, we apply Functional Principal Components Analysis (FPCA) to extract the most meaningful functional features for network time series, pruning those with low informational importance. We compare such algorithm with other state-of-the-art proposals, and show that it achieves lower error for the representation of atypical observations even with higher compression ratios.