Quick Fix:
Stick to ARIMA for non-seasonal, stationary data; switch to SARIMA if seasonality’s creeping in as of 2026.
What’s going on here?
Forecasting algorithms live or die by your data’s quirks. Linear models? They’re fine when relationships are simple and clean. ARIMA variants? They’re built for time-ordered data that’s got trends or repeats every year. Causal models? Those shine when outside factors—like price swings, weather, or policy changes—are driving things, though they demand way more inputs and careful tuning. Pick the wrong one and your error rates (MAE or RMSE) will scream at you, usually because you missed autocorrelation or your data wasn’t steady.
Here’s how to fix it
Step 1 – Diagnose the data
- Plot the series and look for:
– A trend (is it climbing or falling over time?)
– Seasonality (does it repeat like clockwork?)
– Stationarity (does the mean and variance hold steady?) - Run the Augmented Dickey–Fuller test (p-value under 0.05 means it’s stationary).
Step 2 – Pick your model family
| Pattern | Recommended Model | Python Library (2026) | Key Parameters |
|---|---|---|---|
| No trend, no seasonality | AR(p), MA(q), ARMA(p,q) | statsmodels.tsa.arima.model.ARIMA | p, q order; no differencing |
| Trend, no seasonality | ARIMA(p,d,q) | statsmodels.tsa.arima.model.ARIMA | d = 1 (first difference) |
| Trend + seasonality | SARIMA(p,d,q)(P,D,Q)s | statsmodels.tsa.statespace.sarimax.SARIMAX | P,D,Q,s = seasonal period |
| External drivers | VARX(p) or Prophet with regressors | statsmodels.tsa.vector_ar.var_model.VAR or prophet | Number of lags p; list of exogenous variables |
Step 3 – Fit and validate
- Split your data: train on the first 80 %, validate on the last 20 %.
- Fit the model; save those coefficients.
- Generate forecasts on the validation set.
- Compute MAE and RMSE; if MAE’s over 15 % of the series mean, go back to Step 1.
Still not working?
Try these fixes in order:
- LSTM/Transformer – Fire up TensorFlow/Keras when ARIMA residuals still whisper autocorrelation (p < 0.05 on Ljung–Box). Just expect training to take forever.
- Prophet – Facebook’s open-source baby spots seasonality and holidays automatically; perfect for business KPIs where the calendar rules everything.
- Causal Impact – Use this when an event—like a marketing blitz or policy shift—needs quantifying; it runs a Bayesian structural time-series model.
How to keep this from happening again
- Keep a data-quality log: missing dates, weird outliers, and unit changes will wreck your models.
- Re-train pipelines every quarter; economic regimes shift every 3–5 years, says the IMF.
- Store model artefacts (pickle or ONNX) with SHA-256 hashes so you can reproduce results exactly.
- Document the forecast horizon and confidence intervals so stakeholders know the ± bounds as of 2026.
