MASE (Mean Absolute Scaled Error) Calculator
Scale-free forecast accuracy benchmarked against a naive forecast — the metric that works on any series, zeros included.
MASE answers the only question that matters: are you beating the dumbest possible forecast? A MASE ≥ 1 means your sophisticated model is no better than 'tomorrow = today'. It's scale-free, defined for series with zeros, and the metric the M-competitions standardized on.
Formula
About MASE (Mean Absolute Scaled Error) Calculator
Every forecasting metric eventually faces one humbling question: is your model actually better than predicting that tomorrow equals today? MASE answers it directly by dividing your model's error by a naive forecast's error. Below 1 means you beat the naive baseline; at or above 1 means your sophisticated model adds no value over the simplest possible guess — a result far more common than people admit. MASE is scale-free, works on series with zeros (where MAPE fails), and is the metric the M-competitions adopted as their standard. This calculator computes it from the two MAEs.
How to use MASE (Mean Absolute Scaled Error) Calculator
- 1Enter your values into MASE (Mean Absolute Scaled Error) Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use MASE (Mean Absolute Scaled Error) Calculator?
- ✓Computes MASE (Mean Absolute Scaled Error) instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: MASE = model MAE / naive-forecast MAE — < 1 means you beat 'predict the last value'; > 1 means you don't.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What is the 'naive forecast' MASE compares against?+
For non-seasonal data, the naive forecast is 'next value = current value' (a random walk). For seasonal data, it's 'next value = value one season ago'. You compute that naive forecast's MAE on your training data, and MASE scales your model's error against it. Beating this trivial baseline is the minimum bar for a useful model.
Why is MASE better than MAPE?+
It's defined when actuals are zero (MAPE isn't), it's symmetric (MAPE punishes over-forecasting more), and it's scale-free AND benchmarked, so it's comparable across series of wildly different magnitudes while also telling you whether you're beating naive. MAPE tells you percentage error; MASE tells you whether you have skill.
What MASE value should I aim for?+
Anything below 1 means skill — you're beating naive. Strong forecasting models on hard series often land 0.5–0.8; below 0.5 is excellent. Above 1 is a red flag that should prompt you to either fix the model or just ship the naive forecast and save the complexity. The M4 competition winners clustered well below 1.
Can I average MASE across many series?+
Yes — that's a key advantage. Because it's scale-free, you can average MASE across thousands of products, regions or SKUs to get a single fleet-wide skill number, something MAE or RMSE (which depend on each series' scale) can't do meaningfully. This is why large-scale forecasting platforms favor it.
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