Quantura research is organized into two product pillars: demand inflection forecasting and production-grade MLOps. Use the note cards to jump to internal pages and keep your implementation decisions consistent across teams.
Reference setup for naive, moving-average, and classical models before introducing advanced architectures.
Open noteDesign for direct multi-horizon outputs with explainability hooks suitable for operations and revenue teams.
Open noteWalk-forward validation standards with leakage checks, confidence calibration, and error decomposition.
Open noteHow Amazon Forecast defines Average wQL, forecast types, and how Quantura maps quantile outputs into a stock point decision.
Open noteHow to derive acceleration, surprise, and regime features from forecast trajectories.
Open notePipeline standards for training automation, validation checks, deployment control, and rollback.
Open noteData quality, feature integrity, drift/skew dashboards, and policy-based retraining triggers.
Open noteTracking conventions that connect datasets, code versions, model artifacts, and production outcomes.
Open noteStart with naive and seasonal-naive baselines, then move into moving averages and ARIMA/ETS. This gives a stable benchmark before adding modern architectures that can hide failure modes.
Train models to emit full forward paths, not single points. Pair sequence encoders with interpretable attention and feature importance so commercial stakeholders can audit forecast drivers.
Use rolling windows with strict time ordering, then report point and interval metrics side by side. Validate that feature snapshots and joins do not leak future information into training or evaluation windows.
The AWS Forecast metrics guide is a strong reference for reading backtests because it lays out how Forecast scores predictors across backtest windows and how it separates quantile forecasts from point-style operating decisions. It covers wQL, Average wQL, WAPE, RMSE, MAPE, and MASE, and the practical reading is simple: lower values indicate better predictors across those reported metrics.
mean plus up to five custom quantiles from 0.01 through 0.99 via the ForecastTypes parameter.
AWS documentation makes two important points: you can request mean as a forecast type, and you can
also use a quantile as the effective point forecast when the cost of underpredicting differs from the cost of
overpredicting. That is why AWS supports mixed forecast-type selections such as 0.01,
mean, 0.65, and 0.99.
In Quantura's SageMaker Canvas stock workflow, the exported prediction files are quantile-first and do not
actually expose a separate mean column, so the operative point forecast is the quantile in the middle. In
practice that middle column is usually P50 or 0.5, and it becomes the anchor for the
business-day comparison logic.
The Quantura point forecast update is: take the first actionable business-day middle quantile in the forecast round and the last valid business-day middle quantile in the forecast, compare them at the second significant figure, and for most stocks you can round those values to the tens place. If the last middle quantile is lower than the first middle quantile at that significance level, use the upper bound as the operative point level. Otherwise use the lower bound. That keeps the decision anchored to the middle quantile while still adapting to the direction implied by the business-day path.
Convert forecast curves into actionable features: first and second derivative (delta, acceleration), surprise versus realized values, and regime filters that suppress low-confidence transitions.
Treat model updates as software releases with deterministic builds, test gates, staged promotion, and automated rollback triggers for unstable live performance.
Monitor feature distributions, input quality, prediction drift, and business KPIs together. Trigger retraining only when quality gates or drift thresholds are breached.
Every model should be reproducible from data snapshot to deployment hash. Link experiment IDs to model registry entries and keep inference lineage for governance and postmortem debugging.
Use this reference architecture for Quantura-aligned deployment: ingestion to monitoring with explicit control points for validation, registration, and rollback.
Expansion points: real-time feature updates, shadow deployments, champion/challenger routing, and automated governance checks for model lifecycle approvals.
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