Time Series Analysis in Python using Machine Learning

Two most popular uses of Time Series Analysis are: Forecasting & Anomaly Detection. This talk will teach you how to use Machine Learning to do Time Series Analysis for Forecasting & Anomaly Detection.

Title:
Tracking the tracker: Time Series Analysis in Python From First Principles

Abstract:

Predicting the future based from the past is an ubiquitous task for most people. This talk takes a machine learning-centric approach to using time series analysis for forecasting and anomaly detection.

This talk covers popular approaches in time series analysis with a clear paedogical manner where maths is explained in layman's terms. The audience would see how Kalman filters which is popular in navigation and robotics can be applied to time series analysis.

What 3 things will they walk away from having learned? A clear understanding of how to formulate a reasonable problem as a time series problem. Understand machine learning approaches to time series analysis. See Python at work. Understand how to implement concepts from research papers. About Kenneth Emeka Odoh:

Kenneth Emeka Odoh ( https://www.linkedin.com/in/kenluck2001/ ) is a Software Engineer, Computer Scientist, & Data Scientist working at a IoT startup.

Schedule:

• 6:00PM Doors are open, feel free to mingle
• 6:30 Presentation start
• ~7:45 Off to a nearby restaurant for food, drinks, and breakout discussions

Getting There:

By transit there a number of high frequency buses (check Google Maps or the Translink site for your particular case) that will get you there.