Comparison of forecasting models concerning MAPE, convergence speed, noise sensitivity, error management, and computational efficiency.

<p>Comparison of forecasting models concerning MAPE, convergence speed, noise sensitivity, error management, and computational efficiency.</p>

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Bibliographic Details
Main Author:	Nasir Nauman (22272299) (author)
Other Authors:	Sheeraz Akram (17251060) (author), Muhammad Rashid (2836613) (author), Arfan Jaffar (19105558) (author), Sohail Masood Bhatti (19105561) (author), Benish Fida (22272302) (author)
Published:	2025
Subjects:	Medicine Sociology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified lower error rates enhanced feature extraction 57 %&# 8722 2385s vs 3100s 21 %, rmse improve forecasting accuracy europe ), iex sparse encoder nar smart grids due outperformed benchmark models lstm reached rmse integrates long short 74 mw ), senarx ), introduces term load forecasting iex ), sparse encoder smart grids india ), term memory time forecasting concurrent forecasting 33 mw 02 mw price forecasting uncertain markets traditional methods strong interdependence senarx achieved paper presents nonlinear autoregression models suitable model approach japan ). genetic algorithm future work first model exogenous inputs electricity prices data noise comparative study characteristics make based optimization 40 %.
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