A Comparative Analysis of Artificial Neural Networks in Time Series Forecasting Using Arima Vs Prophet

Artificial intelligence heavily relies on neural networks, which enable machines to acquire knowledge and make informed choices by processing data inputs. Time series analysis plays a crucial role in enhancing the capabilities of AI systems powered by artificial neural networks (ANNs). This report provides an overview of neural networks, including the basic components, different types, and the training process. The report also explores advanced topics in neural networks, such as transfer learning, generative adversarial networks, and reinforcement learning. Finally, the report concludes by discussing the advancements and challenges in the field, as well as the future trends and prospects for neural networks. The report provides a comprehensive understanding of this complex but crucial aspect of AI. The aim of this piece is to introduce the concept of time series analysis using artificial neural networks and how it can be a powerful tool for modelling and forecasting complex temporal data. This article is targeted towards individuals with basic knowledge of statistics or machine learning who are interested in learning about time series analysis and its application in artificial neural networks. The focus of this article will be on explaining the fundamentals of time series analysis, exploring the capabilities and limitations of artificial neural networks in analyzing temporal data, and providing examples or case studies showcasing their effectiveness. The tone should be informative, clear, and accessible. Avoid jargon as much as possible while ensuring that technical terms are explained. Use examples to illustrate key concepts whenever appropriate.