首页|Recent Findings from Russian Academy of Sciences Provides New Insights into Mach ine Learning (Mobile Network Traffic Analysis Based On Probability-informed Mach ine Learning Approach)
Recent Findings from Russian Academy of Sciences Provides New Insights into Mach ine Learning (Mobile Network Traffic Analysis Based On Probability-informed Mach ine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting out of Moscow, Russia, by NewsRx e ditors, research stated, "The paper proposes an approach to the joint use of sta tistical and machine learning (ML) models to solve the problems of the precise r econstruction of historical events, real-time detection of ongoing incidents, an d the prediction of future quality of service -related occurrences for prospecti ve development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced." Financial support for this research came from RUDN University Scientific Project s Grant System. Our news journalists obtained a quote from the research from the Russian Academy of Sciences, "First, the preliminary clustering based on the finite normal mixt ures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability -informed ML approach in the field of telecommunications networks. Using the real -world datasets from a Portuguese mobile operator as well as public cellular traffic data, the artic le compares this approach with methods such as random forests, support vector ma chine regression, gradient boosting and LSTM. Vector autoregression,informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchm ark. We demonstrate that DGMM-based regression is 6.82-22.8 times faster than LS TM for the dataset. Moreover, DGMM-based regression can achieve better results f or the most important traffic characteristics (average and total traffic, the nu mber of users). For metrics MAPE and RMSE, it surpasses the results of statistic al methods up to 46.7% (RMSE) and 91.5% (MAPE) (medi an increases are 28.0% and 80.1%, respectively), as w ell as for ML methods up to 13.0% (RMSE) and 35.7% ( MAPE) (median increases are 0.39% and 2.5%, respectiv ely). Thus, the use of a probability -informed approach for telecommunication da ta seems optimal for the computational speed and accuracy trade-off."
MoscowRussiaCyborgsEmerging Techno logiesMachine LearningRussian Academy of Sciences
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