Statistical time-series forecasting that runs entirely in your browser. STL decomposition separates trend from seasonality. Holt-Winters exponential smoothing generates 12-month forward projections with confidence intervals. No backend, no data leaving your machine — powered by Python WASM (Pyodide) running locally.
Before forecasting, understand your data. STL (Seasonal and Trend decomposition using Loess) separates your time series into three components: the underlying trend, the seasonal pattern, and the unexplained residual. This reveals whether your growth is real or just seasonal, and whether your data has anomalies worth investigating.
The Holt-Winters method captures level, trend, and seasonality in three smoothing equations. AERA auto-optimizes the alpha, beta, and gamma parameters to minimize forecast error. The result: a point forecast with 80% and 95% confidence intervals that widen as the horizon extends — showing uncertainty honestly.
Understand when your peaks and valleys occur. The seasonal index for each month shows how much above or below average that month typically performs. Q4 spike for manufacturing companies? Summer dip for B2B services? AERA quantifies it precisely so your budget reflects reality, not hope.
No forms, no sales funnel. Just a conversation about what ARIA can do for your sales forecasting.