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Sourcetable supports multiple forecasting methods through StatsModels and scikit-learn. Describe your forecasting goal in plain English and the AI selects the appropriate method.

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ARIMA / SARIMAX

ARIMA models handle trend and noise. SARIMAX adds seasonal components and external variables. The AI automatically:
  • Tests for stationarity (ADF test)
  • Determines optimal (p, d, q) parameters using AIC/BIC
  • Adds seasonal orders when periodicity is detected
  • Generates forecasts with confidence intervals

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Exponential smoothing

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ML-based forecasting

The AI creates time series features (lags, rolling means, day-of-week, month) and trains a regression model. This approach works well when:
  • You have many external predictors
  • The relationship is non-linear
  • Traditional time series models underperform

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Seasonal decomposition

Breaks your time series into:
  • Trend — long-term direction
  • Seasonal — repeating patterns at fixed intervals
  • Residual — unexplained variation
Supports both additive and multiplicative decomposition.

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Confidence intervals

All forecasts include confidence intervals (default 95%). The AI visualizes:
  • Historical data as a solid line
  • Forecast as a dashed line
  • Confidence bands as shaded regions