Design of Financial Time series Forecasting Models Using Deep Learning Techniques

Abstract

Forecasting Bitcoin prices not only aids in speculation policies and financial decision-making newlinebut also donates to academic research, policy formulation, and technological novelty. As the newlinedigital economy swiftly evolves, precise price predictions are crucial for navigating the newlinecomplexities of the cryptocurrency market and capitalizing on evolving opportunities. newlineWhether for short-term speculation or long-term investment, precise forecasts enhance newlineprofitability by allowing investors to exploit market movements and price fluctuations that newlinemay exceed returns from traditional financial instruments. newlineThis thesis delves into the realm of time series data, with a specific prominence on deep newlinelearning models. The proposed method for predicting Bitcoin volatility integrates both deep newlinelearning and statistical techniques, offering a range of noteworthy advantages. Statistical newlinemodels serve as a robust foundation for analysing historical price data, effectively seizing newlinepatterns and trends by incorporating numerous factors influencing Bitcoin volatility, such as newlinetrading volume and market sentiment. These models are often interpretable and newlinecomputationally well-organized, providing probabilistic predictions that offer valuable newlineinsights into potential outcomes and associated risks. As a result, they endure accessible and newlinebeneficial to a wide range of users, from researchers to market practitioners. The initial newlinesection provides a concise overview of the research problem, the methodologies employed, newlineand the main outcomes realized. This summary offers readers a clear and comprehensive newlineunderstanding of the thesis s objectives and the systematic tactic taken to address them. newlineAdvanced forecasting methods, counting deep learning and statistical techniques, significantly newlineprogress predictive accuracy, allowing stakeholders to benefit even from minimal market newlineshifts. Cryptocurrency forecasting typically involves high-dimensional datasets that newlineencompass a wide range of features such as trading volume, technical indicators, market newlinesentiment, an