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name: scienceclaw-benchmark-for38 description: "Use when a task asks to forecast annual country-level economic series several years ahead (for example GDP per capita levels or unemployment rates, horizon 4) for many economies, from a panel of past series and covariate indicators such as growth and inflation, delivered as level forecasts and judged by mean sMAPE (World Bank WDI-style macro panel forecasting; FoR38 of the companion ScienceClaw-Eval benchmark)." metadata: { "openclaw": { "emoji": "📊" } }

Annual macro-panel forecasting (sMAPE)

Task

• Input: a panel table (economy_id, region, income_level, indicator, then one column per period t-31 .. t0, NaN = missing); per item history (n, L) oldest first, its indicator kind, covariates (n, k, L) (the same economy over all kinds) with covariate_indicators, and the kind descriptions (indicator_kinds).
• Deliverable: float (n, H) level forecasts for the H periods after t0 (H = 4 in the companion benchmark), in each item's own unit, finite and non-negative; percentage kinds at most 100.
• Quality: mean sMAPE in percent, 200/H * sum_h |y - yhat| / (|y| + |yhat|), lower is better (scilib.forecast.smape). The no-change (random-walk) forecast is hard to beat on macro series; always report it as the reference. Series that cross zero (growth, inflation) serve as covariates only, since sMAPE is ill-defined there.

Routes

• scilib.macro.fit_predict(train_panel, history, indicator, indicator_kinds=..., covariates=..., covariate_indicators=..., economy_id=..., region=..., income_level=..., horizon=H) (operators annual_panel_forecast_pooled, annual_panel_forecast_with_covariates) forecasts the log-ratio ln(y[t+h]/y[t]) with members from macro.MEMBERS. The default DEFAULT_MEMBERS = (ridge, huber, lgbm_core, robdrift) is equally weighted; weights="auto" fits weights on a rolling-origin backtest and mixes them 50:50 with equal weights; return_info=True adds backtest sMAPE per member, kind and origin.
• Blocks: rolling_backtest, fit_weights, period_profile (cross-economy median change per period, to spot common-shock periods), local_forecast(x, "flat"|"drift"|"revert"|"theta"|"holt", horizon) (operator annual_series_local_forecast).
• Pretrained comparison: scilib.tsfm.forecast(histories, H, quantiles=(0.5,), model="chronos_2") (assets chronos_2, chronos_bolt_base; operator chronos_quantile_forecast); clip to each kind's plausible range. Check tsfm.available() and scienceclaw_tools(operation=weights).

Pitfalls

• No value after the forecast origin may be used. fit_predict restricts the panel to its first n_periods columns (default: the history length), so a backtest on a shortened history never sees later columns; do not append later values to any history or panel.
• A single-origin validation split is a noisy guide: a few dozen values whose targets hold whatever shock hit the last observed periods. Compare members on a multi-origin rolling_backtest (info["backtest_by_origin"]) and check period_profile before replacing the equal-weight default.
• Percentage kinds (unemployment) are the hard part: features from other kinds can be out of distribution after a common shock, so prefer own-series members there.
• Memorisation risk: these are public statistics, and pretrained forecasters or an LLM may have seen the realised values. Never recall or look up realised values; report gains as potentially contaminated unless they hold on a held-out region or economy group.