## 5 Methods for Short-Term Load Forecasting
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Advantage** & **Disadvantage** \\ \hline \hline Time-Series Models & \\ \hline ARMA & High computational speed for stationary time series. & Unable to respond immediately, resulting in a certain lag. \\ \hline ARIMA & Fast computation for non-stationary time series. & Exhibits some lag and is highly sensitive to outliers. \\ \hline SARIMA & Outperforms ARIMA in predicting cyclical datasets. & Lacks the advantage of exogeneous variables. \\ \hline ARMAX & Combines time series and regression for optimization. & Covariate coefficients are hard to interpret. \\ \hline BVAR & Manages large datasets and prior beliefs in modeling. & Sums lower-frequency data, ignores high-frequency data. \\ \hline GARCH & Simple and generates volatility clustering. & Symmetrical at positive and negative prior returns. \\ \hline SEGARCH & Includes autoregressive errors and GARCH variances. & Lags behind other GARCH models in performance. \\ \hline EGARCH & Achieves accuracy via unconstrained likelihood & Volatility, by its very nature, is unpredictable. \\ \hline Exponential Smoothing & \\ \hline SES & More significance to recent observations. & Does not project trends. \\ \hline DES & Projects trends. & Seasonal patterns are not recognizable. \\ \hline TES & Recognizes seasonal patterns. & Captures the multiplicative aspect of seasonality. \\ \hline Regression Models & \\ \hline RM-W-SML & \\ \hline LR & Performs remarkably well for linearly separable data. & Linearity links independent and dependent variables. \\ \hline NLR & Unbiased and produces smaller residuals. & Mass computation required. \\ \hline LgR & Resists overfitting, avoids feature space disturbances. & Tendency for model overfitting in linear regression. \\ \hline NPR & Assumes no parametric form of predictor dependency. & Larger sample size required. \\ \hline SWR & Manages large potential predictor variables effectively. & Variable selection influenced by multi-collinearity. \\ \hline RM-SML & \\ \hline LR & Easy implementation, extrapolation beyond dataset. & Sensitive to outliers and multi-collinearity. \\ \hline IR & Improves variable relationship understanding. & Complicates model with multiple interactions. \\ \hline RRM & Detects outliers in contaminated data. & Less effective with Gaussian residuals than OLS. \\ \hline SWLR & Manages various potential predictor variables. & Estimates high absolute values. \\ \hline MTR & Offers medium-sized functions for flexible responses. & Takes longer time to train the model. \\ \hline CTR & Offers a few large leaves for coarse response function. & Model flexibility is low. \\ \hline FTR & Offers several small leaves for flexible response. & Very leafy tree tends to overfit. \\ \hline LSVM & Finds decision boundary in separable data. & Low flexibility, time-consuming feature selection. \\ \hline QSVM & Capable of separating non-linearly separable data. & Class overlap increases with noisy datasets. \\ \hline CSVM & Effective when classifier’s kernel function is cubic. & Medium-level flexibility. \\ \hline FGSVM & Precise class divisions with a specific kernel scale. & Flexibility decreases with the kernel scale setting. \\ \hline MGSVM & Efficient with a specific kernel scale. & Classifies only medium-complexity data. \\ \hline CGSVM & Makes coarse distinctions between classes. & Classifies low-complexity data effectively. \\ \hline RQGPR & Fit for large datasets, smooth interpolating functions. & Tricky to detect errors in multi-dimensional data. \\ \hline SEGPR & Handles large datasets in higher dimensions. & Interpretability is difficult. \\ \hline M-5/2-GPR & No measure concentration issue in higher dimensions. & Effective within dataset range boundaries. \\ \hline EGPR & Handles smooth functions with minimum error. & Struggles with smooth functions with discontinuities. \\ \hline BT & Capable to capturing complex patterns. & May overfit if the data are noisy. \\ \hline BgT & Increases with the number of learners, high flexibility. & Introduces a loss of interpretability of a model. \\ \hline \hline \end{tabular}
\end{table}