## 3 Analysis of Original Research Articles
### Time Series Models  
Time series models have been extensively applied to predict commodity prices in agriculture, with various approaches demonstrating unique strengths and limitations, as shown in Table 6. [PERSON] et al. [59] compared feedforward neural networks with ARIMA models for price forecasting, highlighting the neural networks' superior ability to reduce errors and capture trends, albeit with the need for careful hyperparameter tuning. Similarly, [PERSON] and [PERSON] [60] identified ARIMA (1, 1, 1) as the most effective model for corn price prediction over ANN and ESM (exponential smoothing models) in Rajasthan markets, though its reliance on historical data posed challenges in adapting to sudden market shifts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{6}{*}{[59]} & \multirow{6}{*}{1996} & \multirow{6}{*}{Live Cattle, Wheat} & & & Neural networks & \\  & & & & achieved 27\% and & \\  & & & Monthly prices & 56\% lower MSE & \\  & & & (1950–1990) & & compared to & \\  & & & (1950–1990) & & ARIMA; MSE 0.087, & \\  & & & & AME 0.154, & \\  & & & & MAPE 4.235 & \\ \hline \multirow{6}{*}{[60]} & \multirow{6}{*}{2016} & \multirow{6}{*}{Corn markets} & & & ARIMA (1,1,1) & \\  & & & & achieved lowest AIC & \\ \cline{1-1}  & & & (1677–17), SBC & & Limited \\ \cline{1-1}  & & & (1685.86), MAD & & adaptability to \\ \cline{1-1}  & & & markets & ANN, ESM & (71.04), MSE & \\ \cline{1-1}  & & & (11,477.78), and & & sudden market \\ \cline{1-1}  & & & & MAPE (5.64), & \\ \cline{1-1}  & & & & outperforming & \\ \cline{1-1}  & & & & other models & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overview of time series model techniques in wheat, rice, and corn price prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Refs. ** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{9}{*}{[61]} & \multirow{9}{*}{2018} & & Monthly prices & ARIMA, ANN, SVR, MARS, ARIMA\(-\)ANN, ARIMA\(-\)ANN, ARIMA\(-\)SWK, ARIMA  
Hybrid models combining ARIMA with computational intelligence techniques have demonstrated enhanced forecasting accuracy. [PERSON] and [PERSON] [61] integrated ARIMA with ANN, SVR, and MARS to predict prices of rice, wheat, and corn, showing that these hybrid models outperformed standalone approaches. Similarly, [PERSON] et al. [62] explored a linear combination model of ARIMA, ANN, and a combined model for wheat prices in China, where ANN excelled in capturing turning points and delivering high mean monthly  
\begin{table}
\begin{tabular}{p{11.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Refs. ** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{6}{*}{[64]} & \multirow{6}{*}{2022} & & & & ARIMA and & & \\  & & & & SARIMA performed well for short-term predictions (Daily MSE 0.0003), BPN & ARIMA and SARIMA models & ARIMA and SARIMA models \\  & & & & SARIMA, SARIMA, weather details & ARIMA, BPN, LSTM & MSE 0.0003), BPN, LSTM & MSE 0.0003), BPN, LSTM & MSE 0.0003) and BPN, LSTM & MSE 0.0003), BPN, LSTM & MSE 0.0003) and BPN, LSTM \\  & & & & & AR-GARCH, SVR, and MLP (0.0003), BPN & MSE 0.0003) & MSE 0.  
returns, while in error measures, the combined model performed better, thus creating some ambiguity. [PERSON] et al. [22] compared ARIMAX-GARCH and SVR models for rice price forecasting across seven Indonesian cities, finding SVR to be superior in most cases, particularly for premium rice, while ARIMAX-GARCH performed better for lower-grade variants. [PERSON] [23] compared ARIMA, SARIMA, ARIMAX, SARIMAX, LSTM, CNN, and LR (linear regression) models for global wheat price forecasting and found that LR had an MAE of 32.23 and an RMSE of 38.97. Decomposition techniques combined with ARIMA have further improved price forecasting in agriculture. [PERSON] et al.