# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 5 Results
### Computational Cost and Complexity  
The computational efficiency of each model was analyzed by measuring the tuning and training times. The evaluated models exhibit diverse characteristics in terms of trainable parameters and structure. The LSTM model, with 70,211 trainable parameters, demonstrates a strong ability to capture patterns in temporal sequences. The simpler RNN features 23,363 trainable parameters, offering a reduced but still effective capacity for sequential data. The Random Forest comprises 177 decision trees with fixed parameters optimized during tuning. The SVC relies on 96 support vectors to define classification boundaries, while the XGBoost model, configured with 50 trees and a maximum depth of nine, includes 25,550 trainable parameters. The KNN uses four neighbors without additional adjustable parameters, whereas the Naive Bayes model employs probabilistic distributions without direct trainable parameters. The Adaboost model combines multiple weak classifiers structured with 45 nodes, 23 leaves, and a maximum depth of eight. The Decision Tree consists  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline LSTM & 97.20\% & 89.27\% & 88.53\% & 88.59\% \\ RNN & 86.92\% & 40.34\% & 44.76\% & 42.27\% \\ Random Forest & 91.07\% & 77.71\% & 65.98\% & 69.60\% \\ SVC & 91.96\% & 81.07\% & 62.99\% & 69.04\% \\ XGBoost & 94.64\% & 85.33\% & 76.32\% & 79.94\% \\ KNN & 93.75\% & 80.52\% & 82.30\% & 80.45\% \\ Naive Bayes & 85.71\% & 56.04\% & 54.60\% & 54.84\% \\ Adaboost & 91.96\% & 75.60\% & 84.60\% & 78.94\% \\ Decision Tree & 91.96\% & 74.55\% & 75.29\% & 74.70\% \\ ANN & 93.75\% & 86.93\% & 69.66\% & 76.28\% \\ Logistic Regression & 91.96\% & 78.02\% & 65.98\% & 70.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **ROC AUC (Curve)** \\ \hline LSTM & 100.00\%, 99.54\%, 98.84\% \\ RNN & 92.80\%, 96.54\%, 91.42\% \\ Random Forest & 97.84\%, 95.74\%, 96.64\% \\ SVC & 96.96\%, 94.16\%, 94.21\% \\ XGBoost & 98.92\%, 96.49\%, 94.77\% \\ KNN & 98.87\%, 97.32\%, 98.60\% \\ Naive Bayes & 83.92\%, 91.41\%, 90.65\% \\ Adaboost & 88.73\%, 86.70\%, 87.94\% \\ Decision Tree & 83.73\%, 87.25\%, 87.66\% \\ ANN & 99.02\%, 89.97\%, 91.40\% \\ Logistic Regression & 99.12\%, 95.12\%, 92.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: ROC AUC for different models.  
of 141 nodes and 71 leaves, optimized to represent its hierarchical structure. The ANN has 2467 trainable parameters, providing a simpler architecture compared to deeper networks. Lastly, the Logistic Regression model is linear, with adjustable coefficients as its only trainable parameters.  
While the proposed model required more training time than traditional machine learning methods, its superior predictive accuracy justifies the computational trade-off. These results emphasize the balance between computational cost and performance, showcasing the efficiency of the proposed approach. Table 3 summarizes the tuning and training times for each model.