Score a forecast against actuals: MAE, RMSE, MAPE, sMAPE, bias and MASE against ...

{ "method": "GET", "queryParams": { "actual": "3,5,2,7", "forecast": "2,5,4,8", "training": "1,3,2,5,4" }, "type": "http" }

{ "example": { "asOf": "2026-10-21T06:18:22.688Z", "degraded": false, "disclaimer": "Screening signal computed from the named public source. Not financial, legal or investment advice; provided as is. Verify at the source before acting.", "errors": [ 1, 0 ], "mae": 1, "mapePct": 36.9047619, "mase": 0.571428571, "meanError": -0.5, "mse": 1.5, "n": 4, "naiveLag": 1, "naiveScale": 1.75, "notChecked": [ "statistical significance of the difference between two forecasts", "forecast intervals or calibration", "…1 more" ], "reasons": [ "error e = actual - forecast; bias = mean(e) (positive means forecasts too low).", "MASE = MAE / mean absolute one-step (or seasonal) naive error on the history; below 1 beats the naive forecast." ], "rmse": 1.22474487, "smapePct": 30, "source": "computed locally (Hyndman and Koehler 2006, Another look at measures of forecast accuracy)", "summary": "Over 4 points: MAE 1, RMSE 1.22474, MAPE 36.9048%, MASE 0.571429.", "verdict": "BEATS_NAIVE" }, "type": "json" }