Modern Time-Series Methods
Time-series, as a field of study, has largely focused on statistical methods that work well under strict assumptions. Specifically, when there is sufficient history, there is little meta-data and a well-formed auto-correlation structure. However, as an applied practitioner I know that most real-world time series problems violate these assumptions. This leaves us with an opportunity to use more modern time series methods, based on machine learning, to overcome these deficiencies.
This session is designed to briefly speak about the unique properties of time-series, how statistical methods work and how and why machine learning (and deep learning) methods can be used to improve accuracy.
Outline/Structure of the Talk
- A brief background on time-series properties
- An introduction to classical (statistical) time series methods
- An introduction to modern (machine learning) time-series methods
- Understanding when to use which method
Participants will learn about why and when to apply either statistical or machine learning time-series algorithms.
Prerequisites for Attendees
An understanding of basic principals in machine learning and time-series modelling will be needed.