Moreover, the attractiveness of the proposed method can be demonstrated by the comparison with eight benchmark forecasting methods.
The effectiveness of the proposed method is illustrated through simulation of benchmark forecasting and identification problems and comparisons with the existing methods.
Through a real-world urban water demand forecasting experiment in Montreal, Canada, we demonstrate the superiority of WDDFF against benchmark forecasting models such as (non-wavelet-based) random walk, multiple linear regression, extreme learning machine, and second-order Volterra series models.
So if our horizon is shorter, we must pick a benchmark by forecasting where we think returns may be.
In addition to these metrics, it is also common to benchmark one forecasting model to another reference model.
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 (Table 3).
Section Comparison of forecasting results compares the performance of the models for the forecasting benchmark dataset.
Section Results and discussion reports the empirical results of the hybrid series and parallel models for a forecasting benchmark dataset.
If your time horizon is very short, like a year, then you must pick a benchmark via simple forecasting, which is tricky (see my Apr. 3 column).
The MHW method performance is compared against that of the also operationally simple SA3 forecasting benchmark, which is recommended in Abeku et al. [32].
