Co-founder & Practice Director, Intellify
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 Kale knows 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 spoke 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.