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

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Podcast: [DataFramed](https://stenobird.com/podcast/dataframed)
Published: 2026-04-20T09:00:00+00:00
Episode link: https://www.datacamp.com/podcast
Audio file: https://dts.podtrac.com/redirect.mp3/cohst.app/pdcst/6G1A6D/episodes.captivate.fm/episode/ac118883-e487-43e5-973f-06261907591c.mp3
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Duration seconds: 3213

## Resource

Scaling time series forecasting requires moving beyond manual modeling toward automated, production-ready pipelines. This discussion explores the transition from traditional statistical methods to modern foundation models and the engineering rigor needed for deployment.

## Highlights
- Main idea: Foundation models for time series are emerging to solve the scalability crisis caused by massive, high-frequency datasets
- Practical takeaway: Production forecasting requires robust monitoring, scheduling, and drift detection to ensure model stability over time
- Failure mode: Relying on automated models without understanding underlying statistics can lead to catastrophic errors in sensitive business domains
- Practical takeaway: Effective communication of forecast uncertainty and visualization is critical for building stakeholder trust
- Main idea: The role of the data scientist is shifting from manual model building to designing complex, automated forecasting architectures

## Topics

Time Series Forecasting, Foundation Models, Machine Learning Operations, Data Science Engineering, Predictive Analytics, Statistical Modeling, Production Pipelines, Model Monitoring

## Chapters
- 1:00 — The Rise of Time Series Foundation Models: An exploration of how new foundation models aim to assist data scientists in handling increasingly complex time series data.
- 5:00 — The Scalability Challenge: Discussing the trade-offs between manual precision and the need to forecast hundreds of thousands of products simultaneously.
- 8:50 — Handling Real-World Volatility: Why traditional models struggle with sudden structural shifts like policy changes or global events like COVID-19.
- 13:10 — Risks of Automated Decision Making: The high stakes of overestimating demand and the dangers of delegating critical decisions to autonomous agents.
- 17:10 — The Legacy of Prophet: A look at the impact of Meta's Prophet library and its role in introducing modern features to the community.
- 21:10 — Backtesting and Model Stability: Using backtesting to ensure models remain reliable even when underlying data distributions change.
- 25:10 — Engineering Production Pipelines: The necessity of building schedulers, data fetchers, and monitoring systems for live forecasting environments.

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## Transcript

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