Introduction
Understanding time series data is critical for identifying long-term trends, seasonal patterns, and anomalies that drive business decisions. In this walkthrough, we demonstrate how Oracle Analytics Cloud (OAC) and Oracle AI Data Platform (AIDP) work together to simplify and operationalize Time Series Decomposition.
Key Highlights
- Decompose raw time series data into Trend, Seasonality, and Residuals
- Configure business-driven seasonality and aggregation logic
- Prepare and validate continuous datasets for accurate analysis
- Detect anomalies using residual analysis
- Visualize insights seamlessly in Oracle Analytics Cloud dashboards
This approach helps organizations move beyond simply understanding what happened to uncovering why it happened — whether due to long-term growth trends, recurring seasonal behavior, or unique one-time events.
Watch the complete :
Refrence Notebook and Guide:
Above Detailed Guide Covers:
- Connectivity setup between OAC and AIDP
- Configuration and decomposition models
- Data preparation and validation pipeline
- Trend, seasonality, and residual extraction
- Storage architecture and visualization in OAC dashboards
A valuable capability for teams focused on forecasting, anomaly detection, operational intelligence, and advanced analytics.
Note: This document is intended solely for community knowledge sharing and demonstration purposes. It is not official Oracle documentation and should not be considered as formal Oracle guidance or support documentation.
