Deep Learning Your Broadband Network at Home
EuroPython Conference via YouTube
Overview
Syllabus
Intro
Outline
Home Network
Anomaly Detection (Naive approach in 2015)
Problem definition
Types of anomalies in time series
Logging Data
Data preparation
Handling time series
Components of Time series data
Seasonal Trend Decomposition
Rolling Forecast
Anomaly Detection (Basic approach)
Anomaly Detection (Naive approach)
Stationary Series Criterion
Test Stationarity
Autoregression (AR)
Moving Average (MA)
Identification of ARIMA (easy case)
Identification of ARIMA (complicated)
Anomaly Detection (Parameter Estimation)
Anomaly Detection Multivariate Gaussian Distribution
Anomaly Detection (Multivariate Gaussian)
Long Short-Term Memory
Summary
Contacts
Patterns in time series
Taught by
EuroPython Conference