Skip to content
Predict.ai
Feature engineering

Turn raw signals into model-ready features.

AI reviews the workspace and recommends compatible transformations. You approve the features; Predict.ai aligns the data and computes the resulting columns through a deterministic, reproducible engine.

Build a clean, reproducible feature grid from signals that arrive at different times and cadences.

Analysis-driven recommendations

Let AI propose the feature plan. Keep control of what is built.

Workspace analysis produces feature recommendations constrained to transformations the platform can execute. Add one in a click, inspect its source signal, or choose a different technique from the full catalog.
  • AI proposes engineered features from the latest workspace analysis
  • Only compatible source fields are offered for each technique
  • Unsupported transformations are rejected rather than approximated
  • Every generated column keeps its technique and source visible
AI proposes; you approve; the deterministic feature engine executes.

Alignment & missing data

Put mixed-cadence signals on one dependable timeline.

Choose the training interval, tolerance, and gap strategy globally or override individual fields. Smart fill, hold-last, interpolation, and zero-fill cover different signal semantics.
  • Align streams, events, and slower business metrics
  • Set ordered fallback strategies for missing periods
  • Override fill behavior for individual fields
  • Preview the resulting grid before training
The same versioned data definition can be reused across training runs.

Lag features

Add prior values from one, two, three, five, or seven periods back.

Rolling statistics

Create moving averages, standard deviation, minimum, maximum, median, and sums.

Changes and momentum

Represent differences, percentage changes, momentum, and rates of change.

Event features

Capture release flags, surprise values, and time since the latest change.

Calendar features

Encode hour, weekday, month, quarter, weekend, and period boundaries.

Advanced transforms

Add rolling z-scores, volatility, cumulative returns, and rolling bands.

Reproducible by design

Feature definitions travel with the dataset.

Engineered features become named columns in a versioned segment. Training runs can reuse the same definition, and teams can review how each input was aligned and derived.

Put your data to work.

Connect your own data or start with a sample. Explore automatic insights, ask questions, engineer features, and build a forecast in the same workspace.

Start with your dataTalk to sales