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Documentation Index

Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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

"Forecast monthly revenue for the next 12 months using ARIMA"
ARIMA models handle trend and noise. SARIMAX adds seasonal components and external variables.
ModelUse case
ARIMANon-seasonal time series with trend
SARIMASeasonal time series (monthly, quarterly, weekly patterns)
SARIMAXSeasonal time series with external predictors (marketing spend, holidays)
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

"Use Holt-Winters to forecast daily sales for the next 30 days"
MethodUse case
Simple Exponential SmoothingNo trend, no seasonality
Holt’s LinearTrend, no seasonality
Holt-Winters (additive)Trend + consistent seasonal amplitude
Holt-Winters (multiplicative)Trend + growing seasonal amplitude

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ML-based forecasting

"Build a gradient boosting forecast using lag features and rolling averages"
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

"Decompose the sales data into trend, seasonal, and residual components"
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
"Forecast quarterly revenue for 2025 with 90% confidence intervals"