# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 5 Results and Discussion
### Experimental Results  
#### 5.2.1 Different Image Generation and Classifier Techniques  
During the experimental phase, we conducted various tests to validate the effectiveness of Wide-TSNet in predicting Bitcoin time-series data. One of the key steps involved generating images with Gramian angular fields. Specifically, we experimented with two types of Gramian angular fields [11]: the Gramian angular summation field (GASF) and the Gramian angular difference field (GADF). As part of the experiment, we also utilized two lightweight convolutional neural network (CNN) models: SqueezeNet [52] and EfficientNet [53]. These models were chosen for their efficiency and performance in image classification tasks. In addition to SqueezeNet and EfficientNet, we experimented with two other lightweight models: MobileNet and ResNet18. However, the results from these models are not included in our discussion. Despite training these models for several epochs, we observed that the validation loss was consistently lower than the training loss. This is typically indicative of underfitting--a scenario in which the model fails to capture the underlying pattern of the data. Therefore, we decided to focus on the models that demonstrated more promising results in our experimental setup. Tables 1 and 2 present a comprehensive comparison of the performance metrics (i.e., the Accuracy and F1 score) of the three distinct convolutional neural network (CNN) models--Wide ResNets, SqueezeNet, and EfficientNet--when applied to the images derived from Bitcoin time-series data using the different image generation approaches. The first table focuses on RGB images, while the second table is dedicated to grayscale images. Table 2 provides a comparative analysis of the performance of the Wide-TSNet model against other models enhanced with the Gramian angular summation field (GASF) and Markov transition field (MTF). The performance was evaluated based on the key metrics: Accuracy, F1 score, Precision, and Recall. The Wide-TSNet model outperformed all other models, achieving an Accuracy of approximately 0.939614, Precision at 0.901232, Recall at 0.900054, and an F1 score of approximately 0.900686. These metrics indicate a high level of Precision and Recall, suggesting that the Wide-TSNet model is highly effective in accurately classifying or predicting outcomes while minimizing false positives and negatives. The combinations of Wide ResNet with GASF and GADF also exhibited commendable performance, with Accuracies above 0.87 and F1 scores exceeding 0.71. This underscores the effectiveness of integrating these enhancement techniques with established models to obtain improved performance metrics. The results obtained with the SqueezeNet and EfficientNet combinations demonstrated that, while they are efficient, there was a noticeable drop in both the Accuracy and F1 scores when compared with the Wide ResNet combinations. This could be attributed to the architectural differences between these models. These results indicate the superiority of Wide-TSNet, in terms of Accuracy, F1 score, Precision, and Recall; however, it also highlights the potential for improving the model's performance through integrating enhancement techniques such as GASF or MTF. This offers insights for future research directions, with the aim of optimizing machine learning models for increased accuracy and efficiency. In addition to the performance metrics, the computational efficiency of a model also plays a crucial role in its practical applicability. Table 3 shows the computational and time performance of the CNN models employed. While demonstrating superior Accuracy and F1 score, the models that employed Wide ResNet--including Wide-TSNet--required a minimum of 15.36894 gigabytes (GiB) of memory for a batch size of 32 and 13 min and 36 s for a 40-epoch run. This indicates a higher computational demand when compared to the other models. The SqueezeNet models, on the other hand, required a minimum memory of 13.1 GiB and took an average of 10 min for a 40-epoch run.