Loading...

Ronald Menich, Chief Data Scientist, Predictix, LLC @ MLconf NYC

791 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Apr 1, 2015

Retail Demand Forecasting with Machine Learning: For over two decades, time-series methods, in combination with hierarchical spreading/aggregation via location and product hierarchies, and subsequent manual user adjustments, have been a standard means by which retailers and the software vendors who serve them have created demand forecasts. The forecasts so produced are and were used as inputs to store and vendor replenishment, regular and markdown pricing, and other downstream decision support systems. The rise of machine learning — the advent of high-powered commercial product recommender systems such as books at amazon book and movies at netflix, of powerful search (e.g., google), text processing (e.g., Facebook) and sentiment analysis capabilities, IBM Watson, self-driving cars and the like — is real phenomenon based on academically-sound and industrially-proven techniques whose application to retail demand forecasting is ripe.

Loading...

When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...