Every machine learning or classical forecasting method incorporates some statistical assumptions. Data scientists examine the sample data to understand its statistical attributes.

Forecasting 2.0 utilizes attributes of all products, not just ‘new shirt’ and ‘old shirt.’ So instead of having the sales history of an ‘old shirt’ for three years (12 months x 3 years equaling to a ...

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based ...

Artificial intelligence-driven algorithms can be used to better forecast models for natural disasters, saving lives and protecting property by rapidly analyzing massive data sets and identifying ...

The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.