The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science, Apple

In a year from now, my kids could go and do a forecast without knowing anything. Now we can really handle more datasets with less effort. Think about like Walmart, they have thousands or maybe hundreds of thousands of products that they are selling on their stores.

A staple task of data scientists is forecasting, that is trying to predict the future. The tools of this haven't changed in almost a decade until now. A new crop of time series foundation models is emerging, and I want to know how they might help data scientists. And since the real world is messy, a persistent problem of time series analysis has been getting models into production. So I want some advice on that too.

I'm not sure if people would feel comfortable yet to give the power of those decision-making for autonomous agent. You could end up without ability to produce electricity and then cause for a lot of unhappy people.

Our guest is Ramy Crispin, a senior manager of data science and engineering at Apple. Ramy's team creates data products for Apple Finances services and infrastructure. He's the author of Hands-on Time Series Analysis with R and the forthcoming Applied Time Series Analysis and Forecasting. Ramy also created the DataCamp course Designing Forecasting Pipelines for Production.

When it's doing great, it's great, but you need to understand that when you're using auto models, auto algorithms, that sometimes you get it wrong.

And let's make some forecasts about forecasting.