# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 6 Conclusions  
The paper presented Wide-TSNet, a novel hybrid approach for Bitcoin price prediction using time-series data and convolutional neural networks (CNNs). The study introduced a tripartite classification system, comprising 'increase', 'decrease', and'stable' classes, in order to accurately represent Bitcoin price trends. Image-based techniques are employed to transform time-series data into Markov transition fields (MTFs), which are enhanced \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Paper** & **Best Method** & **Data Set** & **Accuracy** \\ \hline Wide-TSNet & Wide-TSNet & Bitcoin & 0.94 \\ [PERSON] et al. [54] & MLT + ANN & Bitcoin & 0.63 \\ [PERSON] et al. [55] & LSTM + GRU & Bitcoin & 0.55 \\ [PERSON] et al. [56] & LSTM & Bitcoin & 0.53 \\ [PERSON] et al. [57] & RF + MLP & Bitcoin & 0.55 \\ [PERSON] et al. [58] & Support Vector Machine (SVM) & Bitcoin & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of related research results. through histogram equalization for better pattern discernibility. The results of this study demonstrate the effectiveness of Wide-TSNet in predicting Bitcoin price movements. The research also suggests that lightweight CNN models, such as SqueezeNet and EfficientNet, present comparable performance to more complex models in certain configurations. The Wide-TSNet model showcases a promising research direction relating to financial time-series prediction, with particular application in the volatile cryptocurrency market. Through leveraging image-based CNNs, the model can achieve high accuracy and F1 scores, indicating its potential as a reliable tool for investors and analysts. However, the results of this study also revealed the importance of image resolution settings and the impact of image enhancement techniques on model performance. In future research, we hope to explore the following areas:  
* Further, lightweight CNN architectures and their configurations will be investigated to enhance model performance while maintaining computational efficiency. * We intend to develop advanced methods for feature extraction from time-series images to improve the model's ability to identify subtle trends and patterns. * We aim to investigate the feasibility of employing various test-to-train data splits, along with additional evaluation metrics. These endeavors would facilitate a more comprehensive and nuanced analysis. * The presented model will be implemented in a real-time trading environment to assess its practicality and responsiveness to live market conditions. It is crucial to further explore the practical implementation of the model in real-world applications. This will help understand the model's performance and effectiveness outside of a controlled environment. * The model's application will be extended to other financial instruments and markets in order to validate its versatility and robustness across different economic scenarios. In addition, insights from the finance, economics, and computer science fields will be combined to refine the model's predictive capabilities and understand the underlying factors influencing price fluctuations. By addressing these suggestions, future research can build upon the foundation laid by Wide-TSNet, potentially leading to more sophisticated and accurate predictive models for financial time-series data. The ultimate goal is to provide a tool that not only forecasts prices but also offers insights into the mechanics of market movements, thus contributing to a more stable and informed financial ecosystem. Conceptualization, P.T.Y. ; methodology, P.T.Y. ; software, P.T.Y. ; validation, P.T.Y. and T.Z. ; formal analysis, P.T.Y. ; investigation, P.T.Y. ; resources, P.T.Y. ; data curation, P.T.Y. ; writing--original draft preparation, P.T.Y. ; writing--review and editing, P.T.Y., T.Z. and P.K.G. ; visualization, P.T.Y. ; supervision, Y.L. ; project administration, P.T.Y. ; funding acquisition, P.T.Y. All authors have read and agreed to the published version of the manuscript.