# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 2 Related Work  
The following section explores the existing literature on time-series classification. This body of work is vast and varied, encompassing a range of methodologies from time-warping [14; 15; 16] and feature-based methods [17; 18; 19; 20] to ensemble-based methods [21; 22], deep neural networks, and hybrid methods [23; 24; 25]. Each approach produces unique insights and challenges, contributing to the rich tapestry of research. Through examining these works, we aim to contextualize our research within this broader academic landscape, identifying where our work aligns with, diverges from, and builds upon these established methods. For this study, we will primarily emphasize research that employs deep learning models. Deep neural networks (DNNs) have revolutionized the field of time-series classification. They leverage models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to automatically extract features from raw time-series data, capturing complex patterns and dependencies. In addition, the novel approach of using time-series images for classification has gained traction. This method transforms time-series data into image format, which can then be processed using image-based deep-learning techniques. The transformation process can involve various techniques, such as Gramian angular fields (GAFs), recurrence plots (RPs), and Markov transition fields (MTFs), each providing a unique visual representation of the time-series data. They can capture local features through their convolutional layers and global features with pooling layers. This combination allows CNNs to effectively handle the complexity and variability of time-series images. Using time-series images for classification combines the strengths of time-series analysis and image classification techniques. This approach enhances the feature extraction capabilities of DNNs and broadens the scope of methodologies that can be applied to time-series classification problems. Two-dimensional (2D) GAF data together with one-dimensional (1D) time-series data have been used in a dual classification system for high-performance gas classification [26]. The gas sensor array (GSA) data set was used to evaluate the AlexNet model for 2D GAF data and an improved version of GasNet for 1D data. The modified GasNet model achieved a state-of-the-art Accuracy of 96.0% on time-series data, while AlexNet achieved an 81.3% test Accuracy in GAF classification. A GAF [27] has also been utilized to classify energy micro-moments and small contextual data points in time-series. The system, designed for edge computing efficiency, can classify up to 7 million GAF-converted data points with an approximate 90% Accuracy in less than 30 s, indicating potential for industrial adoption in edge Internet of Energy applications. Recurrence plots for the classification of time-series [28; 29; 30] have been used in various forms, all of which leverage deep neural networks for classification. The first paper proposed a novel method for time-series classification (TSC) using multi-scale signed recurrence plots (MS-RP) and fully convolutional networks (FCN). This method effectively handles the variability in the distinctive region scale, the length of sequences, and the tendency confusion problem. The second paper addressed the limitations of RP and TSC networks in terms of handling the scale and length variability of sequences by proposing a new method, MSRP-IFCN, which includes a multi-scale signed RP (MSRP) and the inception fully convolutional network (IFCN). The MSRP enhances the scale of images and represents long sequences, while the IFCN improves multi-scale feature extraction. The method had a superior performance on 85 UCR data sets, indicating its effectiveness. In the third paper, the authors transformed the time-series into a black-and-white RP image. A convolutional neural network was then used to classify the image. [PERSON] et al.