forecasting-methodslisted

# Forecasting — methods, metrics, and model choice Task 3 carries **35 of 100 marks**. This skill covers the arithmetic and, more importantly, the judgement the marks actually reward. ## Run the engine, do not hand-calculate py .claude/skills/forecasting-methods/scripts/forecast.py --markdown Every method, every error metric, the 2026 forecast, the long-run projection and the associative regression come out of one command. Useful variants: | Command | Purpose | |---|---| | `--series passengers` | One series only | | `--from 2013 --to 2019` | Pre-COVID fitting basis | | `--from 2022` | Post-COVID fitting basis | | `--long-to 2036` | Projection horizon (default 2036) | | `--associative` | Only the passengers-on-movements regression | | `--at 500000,550000,600000` | Movement levels for the associative forecast | | `--alpha 0.1,0.2,0.3,0.5` | Smoothing constants to compare | | `--plain` | Console-friendly output instead of markdown tables | Numbers typed by hand into prose drift from the workbook and get caught. Generate, then quote. ## The methods ### Naive F(t+1) = A(t). Zero parameters. Its only role is as a **benchmark** — a sophisticated model that cannot beat naive is not earning its complexity. Reporting that comparison is a mark of a careful analyst. ### Simple moving average F(t+1) = mean of the last *n* actuals. Smooths noise; **lags a trend**, and the lag grows with *n*. On a rising series it under-forecasts systematically, which shows up as a positive CFE