Skip to content

Quickstart

Get up and running in 60 seconds.

Generate a sample project

timefence quickstart churn-example
cd churn-example

This creates a self-contained directory with:

  • timefence.yaml — Project configuration
  • features.py — 4 feature definitions
  • data/ — Synthetic parquet files (users, transactions, labels)
  • data/train_LEAKY.parquet — Pre-built dataset with planted leakage
  • README.md — Next-step instructions

Audit the leaky dataset

timefence audit data/train_LEAKY.parquet

TEMPORAL AUDIT REPORT
Scanned 5,000 rows

WARNING  LEAKAGE DETECTED in 3 of 4 features

  LEAK  rolling_spend_30d
        1,520 rows (30.4%) use feature data from the future
        Severity: HIGH

  LEAK  days_since_login
        4,909 rows (98.2%) use feature data from the future
        Severity: HIGH

  OK    user_country - clean (5,000 rows)
  OK    account_age_days - clean (5,000 rows)

Build a clean dataset

timefence build -o train_CLEAN.parquet

Building training set...

  Labels     5,000 rows from data/labels.parquet
  Features   4 features

  Joining with point-in-time correctness (feature_time < label_time):

  OK  user_country         5,000 / 5,000 matched
  OK  account_age_days     5,000 / 5,000 matched
  OK  rolling_spend_30d    5,000 / 5,000 matched
  OK  days_since_login     5,000 / 5,000 matched

  Written   train_CLEAN.parquet (5,000 rows, 7 cols)

Verify it's clean

timefence audit train_CLEAN.parquet
# ALL CLEAN - no temporal leakage detected

Next steps