Designing Time-Series Projections¶
Many of the most valuable AI and machine learning applications rely on time-series data – sequences of observations or events captured over time.
With Event Sourcing, you can create time-series projections directly from your immutable event history, ensuring that each data point is accurate, complete, and reproducible. These projections turn raw events into timelines that reveal how your domain evolves.
Why Time-Series Data Is Powerful for AI¶
Time-series projections are the foundation for many analytical and AI-driven capabilities. They can be used to:
- Forecast demand, usage, or workloads
- Detect anomalies such as sudden spikes or drops
- Reveal seasonal patterns, long-term shifts, or cyclical behavior
Because Event Sourcing preserves the exact order of events, you can produce timelines that are both consistent and aligned with the real-world sequence of occurrences. This makes the resulting datasets highly reliable for both visualization and modeling.
Example: Loan Activity Over Time¶
In the library domain, you might build a projection that tracks loans per title per week. It would draw on BookBorrowed and BookReturned events, and could include:
- The number of active loans
- The average loan duration
- The overdue rate per week
Such a projection could power forecasts for upcoming demand, identify anomalies like sudden increases in overdue returns, and support planning for acquisitions or staffing based on seasonal borrowing patterns.
Designing Robust Time-Series Projections¶
To be effective, time-series projections should follow a few core principles:
- Point-in-time correctness – each data point includes only information available at that moment in history
- Consistent intervals – fixed buckets such as daily or weekly make results easier to use in models and visualizations
- Complete coverage – no missing periods, so trends are reliable
You can also enrich them with derived measures such as moving averages or rolling sums, which highlight trends and smooth out short-term fluctuations.
AI/ML Connection¶
Well-constructed time-series projections give AI models a stable, temporally consistent foundation. Forecasting techniques like ARIMA, Prophet, or deep learning architectures such as LSTMs and Transformers depend on this stability to make accurate predictions.
Anomaly detection models benefit from a reliable historical baseline to compare against. Even in non-time-series tasks, aggregated values from these projections often serve as high-value features that improve accuracy and interpretability.
Next up: From Projections to Features – transform analytical and time-series projections into meaningful, AI-ready variables.