Generic forecasting models assume the year is smooth. In the Gulf it is not. Ramadan moves roughly eleven days earlier each Gregorian year and reshapes demand for a month, and a naive model treats the resulting spike as either noise or a permanent trend. Both interpretations are wrong.
Pick the model with evidence, not taste
Rather than committing to one method, a good engine backtests candidates on the history it already has. Ordinary least squares captures a clean trend; Holt-Winters captures seasonality when there is enough history to learn it. A rolling-origin backtest measures each on out-of-sample error, lets the data choose, and reports the resulting error so the forecast arrives with a known accuracy rather than false confidence.
Clean the inputs first
- Exclude the in-progress month, which is always partial and drags the trend down.
- Dampen extreme spikes so a single viral day does not become the model’s new baseline.
- Add an explicit Ramadan regressor keyed off the Islamic calendar so the seasonal bump is modelled, not smoothed away.
A forecast without a measured error bar is just a confident guess.
Make it auditable
The final discipline is snapshots. Store what the model predicted, then compare it against what actually happened, month after month. Over time you accumulate a predicted-versus-actual track record that tells you whether to trust next month’s number, turning forecasting from a party trick into a planning tool.