baseline-forecasting

baseline-forecasting is a Time series & forecasting skill for AI agents, published by skforecast in the skforecast repository on GitHub. Skillspace rates baseline-forecasting D (59/100) and it passed the safety scan; the rating is based on static analysis only.

Builds and evaluates simple baseline forecasts with ForecasterEquivalentDate (equivalent-date / seasonal-naive / moving-average) to benchmark machine learning models. Covers offset (int vs pandas DateOffset), n_offsets with agg_func, backtesting a baseline, comparing against ML with MASE, conformal intervals, and grid_search_equivalent_date. Use when the user wants a baseline, a naive or seasonal reference, or to check whether a model beats a simple rule. Not for choosing among ML forecasters (see choosing-a-forecaster) or tuning ML hyperparameters (see hyperparameter-optimization).

Grade

D

Quality

59 / 100

Safety

Passed

GitHub stars

1,533

Unproven: rated by static analysis only, not yet compared head-to-head.

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