Speaker "Prasad Jogalekar" Details Back



Predictive Analysis of Correlated Time Series with Multiple Seasonality Components


Many Key Performance Indicators (KPIs) in complex systems such as cellular networks are presented to the analytics system as a set of cross-correlated time-series. Many of these time-series have multiple inbuilt seasonalities, and have time-shifted inter-dependencies among themselves that are not evident. We present a case study based on a real-life example of an LTE network to better understand the seasonality and predict future values of the KPIs.


Dr Prasad Jogalekar works as a Chief Architect of AI/ML for Ericsson, Santa Clara. He holds a PhD in optimization of cloud architectures, and has worked for various startups and Fortune 500 companies in the domain of networking, storage and applying AI/ML techniques for design and performance optimizations.