Comprehensive Review of Different Time Series Forecasting with Case Studies

Abstract— Time series forecasting plays an important role in financial data analysis, enabling informed decision-making for wealth and asset management, trading, risk management, and economic planning. Over time, forecasting techniques have evolved from traditional statistical models to deep learning with advanced architectures. Financial platforms like Bloomberg and Reuters employ diverse forecasting methodologies catered to the complexities of financial markets, including ARIMA, GARCH, gradient boosting, and deep learning models like RNNs and Transformers. These methods are further enhanced by integrating sentiment analysis and alternative data sources, such as shares articles, news sentiment and social media trends, using natural language processing (NLP).This paper examines the scale of time series forecasting techniques, with a focus on their application in financial data analysis. By exploring statistical methods, machine learning approaches, hybrid models, and recent advancements in AI, the paper highlights their strengths and limitations. Specific examples from Bloomberg and Reuters illustrate the practical implementation of these techniques, showcasing their efficacy in handling large-scale, real-time data streams for price prediction, volatility modelling, and market trend analysis.The study also addresses challenges like data stationarity, noise, and distribution shifts in financial time series and discusses emerging trends, including diffusion models and reinforcement learning. By providing a comprehensive overview, this paper serves as a resource for researchers and practitioners aiming to leverage advanced forecasting techniques for financial decision-making.