Table 7.

Checklist for Addressing Recommended Evaluation Procedures.

No.	Recommendation
(1)	Specify which dataset(s) are used in the analysis.
(2)	Specify the aggregation interval(s) used.
(3)	Indicate whether the approach is multivariate or univariate.
(4)	Clearly state if not all metrics are used.
(5)	Document all preprocessing steps, including filtering, normalization, and handling gaps in time series.
(6)	Ensure the training phase starts from the beginning of the dataset’s time frame (2023-10-09).
(7)	Specify the duration of the training window.
(8)	Define and describe the validation window if employed.
(9)	Clearly describe the retraining process if the model is retrained during the evaluation phase.
(10)	Specify the forecasting horizon (length of time into the future for predictions).
(11)	Clearly specify the evaluation metrics used in the article.
(12)	Provide an overall comparison across each time series using statistical distributions and aggregate statistics.
(13)	Assess and document the computational requirements and deployability of the model.
(14)	Make source codes of your experiments and model publicly available for the community.