Zero shot forecasting: finding the right foundation model for O11Y forecasting
a year ago
- #foundation-models
- #time-series-forecasting
- #machine-learning
- Shift from classic statistical methods to foundation models in time-series forecasting.
- Foundation models promise zero-shot and transfer learning capabilities for time-series data.
- Benchmarked models include Amazon Chronos, Google TimesFM, IBM Tiny Time-Mixers, and Datadog Toto.
- Evaluation focused on MAPE (Mean Absolute Percentage Error) for robustness and interpretability.
- Dataset used real-world Kubernetes pod metrics to reflect production challenges.
- Datadog Toto performed best in multivariate forecasting tasks.
- Classical models like Vector-ARIMA remain competitive for steady-state workloads.
- Foundation models excel in handling data variety and reducing operational overhead.
- Trade-offs include inference latency and robustness to unseen data patterns.
- Conclusion: Foundation models are a valuable addition but not a universal solution.