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Empirical · Model class
Single-series forecasting where one macro variable is read against its own past -- trend, seasonality, persistence, and shocks.
What this class is for
Use this class when you need a baseline for one macro series and you want the read to come from the series itself, not from a wider system.
Models in this class
Each model below has its own reference page with overview, graph, proof, and comparison material.
Autoregressive integrated moving average -- the workhorse univariate forecaster for stationary or differenced macro series.
Best for: A clean baseline for a single macro series when you want a transparent read driven by its own past.
Seasonal ARIMA -- extends ARIMA with explicit seasonal AR, differencing, and MA terms for series with calendar structure.
Best for: Series with strong seasonal patterns where a non-seasonal ARIMA leaves seasonal residuals on the table.
Error / trend / seasonality state-space framework that decomposes a series into smoothed components and forecasts forward.
Best for: A robust univariate baseline when you want an alternative read to ARIMA that handles trend and seasonality directly.