Part lll: The Essential Guide to Time Series Forecasting – System Architecture

by Debbie Meron

Forecasting at scale isn’t just about algorithms — it’s about building an architecture that runs them reliably, continuously, and without needing a data science team on standby.

Part 3 of our guide takes you inside the technical backbone of Anodot’s Autonomous Forecast. From model training and data storage to orchestration and continuous forecasting, this paper reveals how a forecasting system becomes a fully productized solution.

“Productizing the generation of accurate on-demand or continuous forecasts requires a system architecture and many technical components. Anodot describes the architecture that underlies Autonomous Forecast.”

What’s inside:

Key system components: data store, training, forecasting modules
How Anodot orchestrates LSTM, Prophet, and ensemble models
Apache Airflow, Kubernetes, and persistence layer design
Considerations for building vs. buying a forecasting solution

Missed a step? Catch up with Part 1 and Part 2

Written by Debbie Meron

Debbie Meron is content marketing manager at Anodot. She is passionate about making tech stories accessible and scuba diving - often doing both at the same time.

You'll believe it when you see it

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