WindowBinSize

• Window bin size sets the granularity of the sliding window
• offset_delta determines how many bins to look back
• Together they define the rolling window: offset_delta × window_bin_size
• Example: WEEK + offset_delta=4 creates a 4-week rolling window

• Finer granularity (HOUR): More responsive to recent changes, higher sensitivity
• Coarser granularity (MONTH): More stable patterns, reduced noise
• Medium granularity (DAY/WEEK): Balanced responsiveness and stability

• HOUR: High-frequency models with rapid data changes
• DAY: Standard operational monitoring for most models
• WEEK: Weekly business cycles, batch processing patterns
• MONTH: Long-term trends, seasonal patterns, strategic monitoring

• HOUR = "Hour" - Hourly time bins for high-frequency rolling baselines
• DAY = "Day" - Daily time bins for standard rolling baseline monitoring
• WEEK = "Week" - Weekly time bins for trend analysis and batch patterns
• MONTH = "Month" - Monthly time bins for long-term seasonal pattern detection

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Example

# Daily rolling baseline looking back 30 days
daily_rolling = fdl.Baseline(
    name="daily_rolling_baseline",
    model_id=model.id,
    environment=fdl.EnvType.PRODUCTION,
    type_=fdl.BaselineType.ROLLING,
    window_bin_size=fdl.WindowBinSize.DAY,
    offset_delta=30
).create()

# Weekly rolling baseline looking back 8 weeks
weekly_rolling = fdl.Baseline(
    name="weekly_rolling_baseline",
    model_id=model.id,
    environment=fdl.EnvType.PRODUCTION,
    type_=fdl.BaselineType.ROLLING,
    window_bin_size=fdl.WindowBinSize.WEEK,
    offset_delta=8
).create()

• Ensure sufficient data volume within each bin for statistical reliability
• Finer granularities require higher prediction frequencies
• Consider your model’s prediction patterns when selecting bin size
• Balance between responsiveness and statistical stability