# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 2 Related Work
[31] have proposed an innovative approach that leverages Markov transition fields (MTFs) and deep learning to classify vibration events and measure the vibration frequency in a \(\varphi\)-OTDR-based fiber-optic distributed vibration sensor. The method transforms normalized time-series data from a detected signal into an MTF image, which is subsequently classified using a convolutional neural network (CNN) and a fully connected neural network. Notably, this cost-effective and efficient method outperformed conventional techniques, effectively handling both vibration events and single-frequency vibrations. This study provided a technical reference for applying deep learning to measure the vibration frequency and offers insights into the event classification of vibrations using image processing methods. Deep learning models have found extensive application in handling financial time series datasets. Convolutional neural networks and recurrent neural networks stand out as the most prevalent deep neural network architectures employed for this purpose. Researchers have leveraged these models in various forms to address forecasting and classification tasks related to financial time series data. Their utilization has yielded valuable insights and contributed significantly to the field. In response to the challenges posed by mode mixing in high-frequency financial time series data, a novel classification method based on low-frequency approximate representation is proposed [32]. The approach leverages complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series into modal components and residual terms. By calculating permutation entropy and clustering it into two categories, the method integrates corresponding modal components and residual terms to extract low-frequency information. This adaptive extraction mitigates mode mixing issues, leading to improved classification. Distance matrices (computed using Euclidean distance or dynamic time warping) are then used for classification. To address the speculative nature of Bitcoin, which complicates price forecasting, this research [33] proposes a robust forecasting framework that mitigates noise in Bitcoin time series and evaluates the predictive capabilities of three distinct types of predictors: fundamental indicators, technical indicators, and univariate lagged prices. The framework encompasses a three-step hybrid feature selection process to identify the most predictive variables, followed by the application of Hampel and Savitzky-Golay filters to remedy outliers and eliminate signal noise from the Bitcoin time series. Subsequently, multiple deep neural networks, fine-tuned using Bayesian optimization, are employed to forecast short-term prices for intervals ranging from the next day to seven days ahead. Among the benchmark models evaluated, the Deep Artificial Neural Network model, utilizing technical indicators as input data, outperforms alternative models. The presented findings demonstrate a notable level of accuracy, surpassing all existing models reported in prior literature. This study [34] examines the impact of various augmentation methods on stock datasets using two state-of-the-art deep learning models. The findings demonstrate that the incorporation of augmentation methods leads to significant enhancements in financial performance when combined with a trading strategy. In the case of both relatively small and large datasets, the augmentation methods yielded a remarkable improvement in risk-adjusted return performance. These outcomes highlight the efficacy of augmentation methods in addressing the challenges associated with financial data and underscore their potential for enhancing predictive models in this domain. This paper [35] introduces a novel tri-state labeling approach to classify the underlying patterns in price data, categorizing them as up, down, or no-action. The inclusion of a no-action state in this approach reduces the need for denoising the dataset as a preprocessing step.