## 3 Analysis of Original Research Articles
### Time Series Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Refs.** & **Year** & **Commodity** & **Model Type** & **Technique** & **Evaluation Metric** & **Performance** \\ \hline
[56] & 2017 & \begin{tabular}{c} Wheat, \\ Corn, Soybean \\ \end{tabular} & DL Hybrid & \
\begin{tabular}{c} VMD– \\ PSO-BPNN \\ \end{tabular} & MAPE/MAE/RMSE &
\begin{tabular}{c} Wheat 0.55\%/ \\ 2.68/3.41 \\ Corn 0.57\%/ \\ 2.12/2.82 \\ \end{tabular} \\ \hline
[57] & 2023 & Wheat & DL & BP–LSTM & MSE & 0.00026 \\ \hline
[59] & 1996 & Live Cattle, Wheat & ML, TS & ARIMA, FFNN & MSE/AME/MAPE &
\begin{tabular}{c} Wheat: 0.087/ \\ 0.154/4.235 \\ \end{tabular} \\ \hline
[60] & 2016 & Corn & TS, ML & ARIMA (1,1,1) & \
\begin{tabular}{c} AIC/SBC/MAD/ \\ MSE/MAPE \\ \end{tabular} &
\begin{tabular}{c} 1677.17/1685.86/ \\ 71.04/11,477.78/ \\ 5.64 \\ \end{tabular} \\ \hline
[61] & 2018 & Rice, Wheat, Corn & TS, ML & \
\begin{tabular}{c} ARIMA–ANN \\ ARIMA–SVR \\ ARIMA–MARS \\ \end{tabular} & MAE/RMSE/MAPE &
\begin{tabular}{c} Rice: 10.365\%/ \\ 12.037\%/2.650\% \\ Wheat 9.779\%/ \\ 11.371\%/4.347\% \\ Corn: 6.495\%/ \\ 9.102\%/3.785\% \\ \end{tabular} \\ \hline
[62] & 2007 & Wheat & TS, ML & \begin{tabular}{c} ARIMA, ANN, \\ Combined \\ mod-el \\ \end{tabular} & \
\begin{tabular}{c} MAE/MSE/ \\ MAPE/MMRR/Dstat \\ \end{tabular} &
\begin{tabular}{c} Rice: 10.365\%/ \\ 12.037\%/2.650\% \\ Wheat 9.779\%/ \\ 11.371\%/4.347\% \\ Corn: 6.495\%/ \\ 9.102\%/3.785\% \\ \end{tabular} \\ \hline
[62] & 2007 & Wheat & TS, ML & \begin{tabular}{c} ARIMA, ANN, \\ Combined \\ mod-el \\ \end{tabular} & \
\begin{tabular}{c} MAE/MSE/ \\ MAPE/MMRR/Dstat \\ \end{tabular} &
\begin{tabular}{c} Combined model \\ had the lowest \\ MSE (143.045), \\ MAPE (0.545\%); \\ in MMRR and \\ Dstat, ANN had \\ the highest: \\ 0.9627/91.667 \\ \end{tabular} \\ \hline
[22] & 2024 & Rice & TS, ML & \begin{tabular}{c} ARIMAX– \\ GARCH, SVR \\ average MAE \\ \end{tabular} & \
\begin{tabular}{c} Average \\ MAPE/average \\ RMSE/ \\ average MAE \\ \end{tabular} &
\begin{tabular}{c} 3.04\%/335.01/ \\ 291.19 \\ \end{tabular} \\ \hline
[23] & 2024 & Wheat & TS/DL & \
\begin{tabular}{c} ARIMAX, \\ SARIMA, \\ LSTM, \\ CNN, LR \\ \end{tabular} & MAE/RMSE &
\begin{tabular}{c} ARIMA, \\ SARIMA, \\ weekly and \\ monthly MSE: \\ 0.0005, 0.0004 \\ \end{tabular} \\ \hline
[63] & 2022 & \
\begin{tabular}{c} Corn, Crude \\ Oil, Gold \\ \end{tabular} & DL + TS & VMD-ARIMA & MAE/RMSE/MAPE &
\begin{tabular}{c} ARIMA, \\ SARIMA daily \\ MSE: 0.0003; BPN \\ weekly and \\ monthly MSE: \\ 0.0005, 0.0004 \\ \end{tabular} \\ \hline
[64] & 2022 & Wheat & DL + TS & \
\begin{tabular}{c} ARIMA, \\ SARIMA, \\ BPN, LSTM \\ \end{tabular} & MSE &
\begin{tabular}{c} ARIMA, \\ SARIMA daily \\ MSE: 0.0003; BPN \\ weekly and \\ monthly MSE: \\ 0.0005, 0.0004 \\ \end{tabular} \\ \hline
[58] & 2024 & Corn & DL Hybrid & \
\begin{tabular}{c} INBEATSx, \\ XGBoost, LSTM \\ \end{tabular} & MAPE/MAE &
\begin{tabular}{c} MAPE/MAE \\ 63.66) and 60-day \\ horizon (MAPE \\ of 3.42 and MAE \\ \end{tabular} \\ \hline
[65] & 2023 & Corn & DL, TS & \
\begin{tabular}{c} Nonlinear Auto- \\ Regressive \\ Neural \\ Network, RW, \\ AR-GARCH, \\ SVR, RT, LSTM \\ \end{tabular} & RMSE &
\begin{tabular}{c} 1.05\% (training), \\ 1.08\% \\ (validation), \\ 1.03\% (testing) \\ \end{tabular} \\ \hline
[24] & 2021 & Wheat & TS, ML & \
\begin{tabular}{c} ARMA, \\ ANN, RW \\ \end{tabular} & RMSE/MAE &
\begin{tabular}{c} 0.6948/0.4627 \\ 0.331/128.674 \\ \end{tabular} \\ \hline
[67] & 2021 & \
\begin{tabular}{c} Wheat, \\ Soybean, Corn \\ \end{tabular} & TS & VEC, s-VAR & MAE/MSE &
\begin{tabular}{c} 7.331/128.674 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._MAPE (Mean Absolute Percentage Error) is the average of the absolute percentage differences between actual and predicted values; it is useful for comparing forecast accuracy across datasets. MAE (mean absolute error) is the average of the absolute differences between actual and predicted values, a straightforward measure of average prediction error.