# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 3 Wide-TSNet
### Classification Layer  
The final layer of the Wide-TSNet framework is the classification layer. In this layer, Wide ResNets are employed to classify images (or patterns within them) based on features learned from previous layers. Wide ResNets are advanced machine learning algorithms known for their accuracy and efficiency in handling complex data sets. The output of this layer is the classification result, which provides the final Bitcoin price prediction.  
Convolutional neural networks (CNNs) [42; 43] revolutionized computer vision by automatically learning spatial hierarchies of features in tasks such as image and video classification. These networks consist of one or more convolutional layers, often followed by pooling, fully connected, and normalization layers. The convolutional layer performs a dot product between its weights and a small region in the input volume, extracting feature maps from the image. Subsequent pooling layers reduce the spatial size to control overfitting. The architecture of a simple CNN model is depicted in Figure 4.  
The CNN architecture is specifically tailored to exploit the inherent 2D structure of input images (or other 2D data, such as speech signals). This is accomplished through the use of local connections and tied weights, followed by a pooling process that yields translation-invariant features. An additional advantage of CNNs is their ease of training and reduced parameter count, compared to fully connected networks with an equivalent number of hidden units. Several CNN variants have been introduced, and in the next section, we outline those selected for this study.  
A residual network (ResNet) is a type of artificial neural network. The key innovation of a ResNet is the introduction of \"Skip connections\" or \"Shortcut connections,\" which allow the network to skip one or more layers. This approach helps to address the vanishing gradient problem, a common issue in training deep neural networks. ResNet has several variants, each with a different number of layers, including ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152, among others. Other studies have further developed ResNet variants, such as ResNeXt, ResNeSt, Res2 Net, and Res2 NeXt5 [44; 45; 46]. As this study focuses on utilizing lightweight CNN models, we focused on ResNet-18. We also used Wide ResNets for comparison with lightweight models.  
Figure 4: A simple convolutional neural network framework.  
Figure 3: Comparison of original and histogram equalized image.  
Wide residual networks (Wide ResNets) [13] are a ResNet variant with decreased depth and width. This novel architecture was proposed to tackle the problem of diminishing feature reuse in very deep residual networks, which makes these networks very slow to train. Wide ResNets also have several variants with different numbers of layers [47]. The naming convention for Wide ResNets is WRN-n-k, where 'n' refers to the total number of convolutional layers and 'k' refers to the widening factor. Wide ResNets have several advantages over standard ResNets, such as having fewer layers and being faster to train. For example, a simple 16-layer-deep Wide ResNets outperformed all previous deep residual networks in terms of accuracy and efficiency, including 1000-layer deep networks. Moreover, they achieved state-of-the-art CIFAR, SVHN, and COCO results, as well as significant improvements in the ImageNet data set.