A time series asks two questions: what is the pattern (trend, season, noise) and what comes next. Magic Stat answers both from a column of dates and a column of values — decomposition, autocorrelation, stationarity tests and a forecast with its confidence band, plus a model comparison that does not flatter itself.
The module decomposes the series (STL: trend, seasonal, residual), describes its dependence (ACF and PACF), tests whether it is stationary (ADF and KPSS, on the level and on the first difference) and forecasts it with a model chosen among Holt-Winters, Theta, ARIMA/SARIMA, ETS, a random-forest lag model, the naive benchmarks or an ensemble.
The trap in forecasting is choosing the model by in-sample fit. Magic Stat compares models by how they do on data they did not see — a holdout split or, better, walk-forward cross-validation across several origins — and reports RMSE, MAE and MAPE from that evaluation.
It is for the classical, univariate and seasonal setting. If you need structural VARs, state-space models with custom components, or the full Box-Jenkins methodology with manual order and diagnostics, statsmodels in Python or a dedicated package is the honest recommendation; the econometrics module covers the intervention-and-breaks side of time series.
Models: Holt-Winters · Theta · ARIMA (auto AIC) · SARIMA (auto seasonal) · ETS · Random Forest (lags) · Ensemble (average) · Naive · Seasonal Naive. Selection: by holdout RMSE or by AIC.
Evaluation: holdout (one split) or walk-forward (rolling origin, 2–20 folds). Horizon: up to 120 steps. Seasonal period is set in the dialog.
By out-of-sample error, not in-sample fit. Use the walk-forward evaluation with several rolling origins — it is the more honest test — and look at RMSE and MAE rather than only MAPE, which is unstable when values are near zero.
For a seasonal series, Holt-Winters or SARIMA; for a series with little structure, Theta and the naive benchmarks are hard to beat. If the comparison shows a naive benchmark winning, that is the honest answer — report it.
Yes — ADF and KPSS, on the level series and on the first difference. They are reported together because a unit-root test and a stationarity test can disagree, and the disagreement is informative.
No — it is a univariate (or seasonal) forecasting module. For multivariate models such as VAR or VECM, the econometrics module and its cointegration tab are the place to look, and for anything beyond that a dedicated package is the honest recommendation.
No. The analysis runs on your machine; the only automatic signal is an anonymous installation counter with nothing in it.