Multiplicative Holt-Winters method: pseudo-parameter and mean absolute percentage error (MAPE) values.

Median (and inter-quartile range) pseudo-parameter α, β, and γ values—which smooth control level, trend, and seasonal time-series components, respectively—reflect fitting of B = 500 bootstrap-generated full-length pseudo-time-series with the seasonal multiplicative Holt-Winters method. The greater the pseudo-parameter value, the shorter the smoothing memory, i.e., information from the recent-past have more pronounced effects on estimates than those from the distant-past. Generally, the strength of pseudo-parameters follows γ≫α≥β, which is expected for time-series with highly seasonal and negligible trend components. Furthermore, large mean absolute percentage error (MAPE) values between observed monthly consultation rates and their median forecasts imply low accuracy and vice-versa. Thus, 92% of the 24 time-series (TS) forecasts generated here (2 forecast horizons, 3 diseases, and 4 age categories = 24 TS forecasts) are reasonably accurate, i.e., their MAPE values are circa 25%. MAPE values from a seasonal adjustment (SA3) forecasting method [32] are also listed for benchmark comparison. The MHW performance is equal or superior to that of the SA3 forecasting benchmark in 87.5% of the 24 TS forecasts generated here, as implied by equal or smaller MAPE values.