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

by Eyal Katz

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

You'll believe it when you see it

Featured resources

Documents 1 min read

A Payment Operations Maturity Guide

Documents 1 min read

Part lll: The Ultimate Guide to Building A ML Anomaly Detection System – Identifying and Correlating Abnormal Behavior

Spotting anomalies is one thing. Making sense of them is another. In Part 3 of our guide, we focus on how to rank and correlate anomalies to uncover what really matters — and cut through alert fatigue in large-scale data environments. “For every anomaly found in a metric, there is a notion of how far […]

Documents 1 min read

Part ll: The Ultimate Guide to Building A ML Anomaly Detection System – Normal Behavior of Time Series Data

Before you can detect what’s wrong, you need to understand what’s normal. Part 2 of our three-part guide focuses on how to model the “normal” behavior of time series data — a critical step in detecting anomalies accurately and at scale. “There are many patterns and distributions that are inherent to data. An anomaly detection […]