Forecasting Economic Trends: Analyzing FRED Data with Time Series Methods and RNNs

Time series analysis remains a vital field in data science, applicable across various domains, including finance, economics, and more. In this research, we use the Gross Domestic Product (GDP) dataset as an example to explore the effectiveness of diverse time series analysis methods and advanced deep learning techniques for forecasting. It’s important to note that our model is capable of handling numerous such datasets, making it a versatile tool for time series analysis and forecasting. Our comprehensive methodology encompasses collecting data from sources like the FRED API, preprocessing the dataset, and systematically applying a wide array of methods. These methods include traditional time series analysis, ARIMA models, and Recurrent Neural Network (RNN) architectures. Our findings highlight the power of modern RNN architectures, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models, in capturing the intricacies within time series data, exemplified here by the GDP dataset. These deep learning techniques, when applied to various datasets, exhibit remarkable accuracy and the potential to provide valuable insights for data-driven decision-making. While traditional time series analysis methods may have limitations in modeling the complex and dynamic nature of temporal data, they still offer valuable insights. This research contributes to the broader discourse in time series analysis, emphasizing the significance of modern deep learning techniques when dealing with intricate time series data. It provides a robust framework for practitioners and researchers to navigate the complexities of time series data analysis and underscores the utility of integrating these techniques for enhanced forecasting capabilities. The insights gained from this study hold the promise of facilitating practitioners and researchers, enabling them to make informed decisions when it comes to forecasting and analysis.