Start with a quick walkthrough or explore the core concepts first.
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Core concepts
System architecture
Explore the overview, module responsibilities, and execution paths.
Runtime and feedback loops
Understand collection, forecasts, advice, refresh, and actual outcomes.
Capabilities and concepts
Understand modules, metric templates, models, and operating policies.
Metrics and data quality
Connect sources, templates, and datasets to prepare dependable inputs.
Models and prediction modes
Choose fixed or dynamic methods, explore models, corrections, and eligibility.
Algorithm reference
Naive
Repeats the latest observation across the horizon. Use it as a basic comparison and to check the data workflow; it cannot anticipate cycles or sudden changes.
Seasonal Naive
Repeats the corresponding observations from the preceding cycle. A seasonal baseline for repeating demand; anomalies in that cycle are repeated too.
EWMA
Smooths recent observations and projects the resulting level. A low-cost candidate for recent demand, without explicit daily seasonality.
Histogram Percentile
Uses a time-decayed historical percentile as a constant future level. Useful for comparing high-water resource sizing, without predicting when a peak occurs.
Random Forest
Fits and averages regression trees on sampled historical features, supporting nonlinear relationships and covariates. More costly than simple statistical methods.
KNN
Finds similar historical windows and follows their subsequent trajectories. Useful for recurring shapes, with limited ability to anticipate patterns absent from history.
Holt-Winters
Models level, trend, and seasonality with additive or multiplicative seasons and optional damped trend. Suitable for cyclic demand with changing levels.
Model service · forecasting and tuning
Calls an external time-series model over HTTP. Configure it in Model management, then choose the module prediction mode. A failed request fails that candidate while other eligible methods continue.
Theta
Combines a smoothed level with half the fitted linear slope, optionally removing seasonality first. A candidate for noisy trends; forecasts are clamped at zero.
MSTL
Separates daily and weekly seasonal shapes and applies Theta to the remainder. This implementation iterates fixed seasonal shapes rather than time-varying LOESS components.
Seasonal Profile
Groups observations by time of day and optionally weekday, then uses a decayed percentile per bucket. Useful for weekday/weekend demand patterns.
Peak Envelope
Builds a rolling-maximum envelope and extrapolates its robust slope. Targets high-water demand rather than averages, such as memory sawtooth patterns and cache growth.
Combination
Combines member forecasts using a stepwise median or recent-error weights. The combination must earn backtest promotion like other candidates.
Ridge AR
Fits linear relationships from lags, optional UTC calendar features, and covariates. A relatively inexpensive machine-learning candidate for stable linear dependencies.
GBDT
Fits successive trees to residuals using lags, calendars, and covariates. Supports squared-error point forecasts or a native quantile objective.
User guides
Backtesting and evaluation
Compare resource forecasts with actual use and understand scores and rankings.
Recommendations and look-ahead
Learn how model consensus, lead time, and constraints shape resource advice.
Admission and scaling
Enable Pod resource advice and replica scaling separately, then review outcomes.
Safety and fallback
Manage schedules, fallback, bypass, and safeguards for automatic actions.
AI assistant
Operations
Dashboard and activity
Review module status, attention flags, and applied scaling activity.
Monitoring and troubleshooting
Trace issues from data sources and model runs to actual replica readiness.
Administration
Manage access, scheduled tasks, audit, and time settings.
Common questions
Find answers about setup, model choice, operating modes, and resource updates.