# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 1 Introduction  
The representation of time-series data is crucial for several analytical tasks, including comparison analysis, clustering, and classification. Conventional illustration methods yield uniform representations derived from statistical data [1]. Moreover, analyzing time-series data on currency values such as bitcoin can provide insights for investment decisions. Time-series data are represented numerically and analyzed using statistical and machine-learning techniques. Numerous studies have recently focused on creating deep learning frameworks to tackle time-series problems. This trend is largely driven by advancements in deep learning technology and the evolution of graphics processing units. Within the cryptocurrency field, a considerable amount of research is being conducted on various machine-learning methods for the prediction of their prices and returns [2]. Predicting the future behavior of complex systems such as financial markets remains challenging. As the leading cryptocurrency [3], Bitcoin exhibits volatile price fluctuations, making accurate price predictions highly desirable for investors and analysts. With Bitcoin at the forefront, cryptocurrencies have transformed the financial scene, offering a decentralized alternative to the old monetary systems. However, the volatile nature of Bitcoin prices [4, 5] poses a significant challenge for investors and market analysts. While technically a cryptocurrency, Bitcoin struggles to function as a traditional currency due toits extreme price fluctuations [6]. These fluctuations make it impractical and expensive to use Bitcoin as a standard of value or a means of transaction. This is true for short-term periods (minutes) and longer durations (days, weeks, and months). However, Bitcoin can serve as a store of value, despite its volatility, when viewed over an extended timeframe. The high volatility negatively impacts Bitcoin's utility as a currency and its viability as an investment. However, even though Bitcoin has experienced significant fluctuations in its value compared to major currencies, it has the potential to develop into a reliable store of value, serving as an alternative to traditional value reserves such as gold. It is these issues related to fluctuations and high volatility that make predicting Bitcoin prices challenging. Most of the existing literature in this context has focused on techniques such as long short-term memory (LSTM) and ARIMA [7]. While models like LSTM and BiLSTM are proficient in identifying time-related dependencies, they can encounter problems such as vanishing and exploding gradients, which can negatively impact their effectiveness [8]. A further challenge with many of these models is that their predictions span a temporal range varying from several hours to weeks, which is contingent upon the time step employed during model training. However, an imperative for accurate predictions within a more condensed time frame has emerged, which is dependent on the time step of the data under consideration. In order to leverage the superior classification capabilities of convolutional neural networks (CNNs), recent studies have converted time-series data into image format, effectively transforming the time-series classification (TSC) problem into an image classification task. This approach amplifies the unique areas of a sequence and establishes temporal correlations, leading to enhanced accuracy [9; 10]. In this paper, we introduce a unique method, Wide-TSNet, for predicting Bitcoin prices using time-series data. The Wide-TSNet method employs a hybrid approach that combines image-based convolutional neural networks (CNNs) with a series of transformations and enhancements. In this approach, we first convert the Bitcoin price data into images. This time-series data approach is often used to forecast future trends and detect temporal patterns. This conversion aims to encapsulate the dynamic relationships between past price points in a visual format, potentially unveiling hidden patterns that may not be apparent in the raw data.