Sentence examples similar to forecast cases from inspiring English sources

There were seven study designs, ordered by the strength of evidence: randomized controlled trial (known as an experimental design), quasi-experimental, multi-group comparison, forecast, case study, descriptive study, and literature review.

The number of switches of the under- and over-forecast cases is also too high compared with the perfect-forecast case, as shown in Fig. 17.

As shown in subplots 2 and 3, Fig. 15, in the over-forecast or under-forecast cases, the PEESS has multiple overshoots or undershoots from ( P_{f}^{text{Baseline}} ) in hour 13.

Open image in new window Fig. 16 Impact of different forecasting accuracies of the feeder baseline load on room temperature profiles   2) As shown in subplots 2 and 3, Fig. 15, in the over-forecast or under-forecast cases, the PEESS has multiple overshoots or undershoots from ( P_{f}^{text{Baseline}} ) in hour 13.

In the other two cases, the ( P_{f}^{text{Baseline}} ) is calculated by adding or subtracting 5% random forecast errors from the perfect-forecast ( P_{f}^{text{Baseline}} ) to represent the over-forecast and the under-forecast cases, as shown in Fig. 14.

The experimental results in five real forecasting cases show that: (a) the proposed hybrid WPD-FEEMD-Elman model has satisfactory performance in the multi-step wind speed predictions; and (b) the hybrid WPD-FEEMD-Elman model has improved the forecasting performance of the hybrid WPD-Elman model and the standard Elman neural networks considerably.

This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies.

In the first case, the feeder hourly load profile, ( P_{f}^{text{Baseline}} ), is calculated as the actual hourly mean of the feeder load, ( P_{f} ), to simulate a perfect-forecast case.

As shown in subplot 1, Fig. 15, in the perfect-forecast case, the PEESS can compensate for the feeder load fluctuations well (with one overshoot at 13.8) without being turned on and off very frequently (as shown in Fig. 17).

We evaluate the capability of the proposed approach by applying it to the forecasting case studies of International Business Machines Corporation IBMM) stock price, and compare the outcomes and the results of the other methods with MAPE metric and Wilcoxon signed ranks test.

The following observations are made from the simulation results: Open image in new window Fig. 15 Feeder load profiles for the three cases 1) As shown in subplot 1, Fig. 15, in the perfect-forecast case, the PEESS can compensate for the feeder load fluctuations well (with one overshoot at 13.8) without being turned on and off very frequently (as shown in Fig. 17).