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  "Description": "Provides a function to calculate multiple performance\nmetrics for actual and predicted values. In total eight metrics\nwill be calculated for particular actual and predicted series.\nHelps to describe a Statistical model's performance in\npredicting a data. Also helps to compare various models'\nperformance. The metrics are Root Mean Squared Error (RMSE),\nRelative Root Mean Squared Error (RRMSE), Mean absolute Error\n(MAE), Mean absolute percentage error (MAPE), Mean Absolute\nScaled Error (MASE), Nash-Sutcliffe Efficiency (NSE),\nWillmott’s Index (WI), and Legates and McCabe Index (LME).\nAmong them, first five are expected to be lesser whereas, the\nlast three are greater the better. More details can be found\nfrom Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and\nGarai et al. (2024) <doi:10.1007/s11063-024-11552-w>.",
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