A novel approach to analyzing Philippine inflation dynamics through persistent homology

This study leverages Topological Data Analysis and Persistent Homology to explore the Philippine inflation data from 1958 to 2023, aiming to uncover complex structures that traditional analysis might miss. By exceeding conventional econometric methods, we sought to reveal intricate cycles and trends within the economic landscape. Our approach revealed a mix of short-lived and persistent topological features within the inflation data, suggesting the presence of underlying economic cycles and seasonal effects with notable implications on inflation trends. Utilizing TDA allowed us to identify and quantify these features through Betti numbers, persistence diagrams, barcodes, landscapes, and silhouettes, providing a comprehensive visual and quantitative framework for understanding the topological structure of the data. The findings underscore the potential of TDA to profoundly enrich economic indicator analysis by uncovering the temporal dynamics and hidden structures within inflation rates. This methodology not only advances economic research techniques but also supports the development of more nuanced economic policies and forecasting strategies. The study confirms that TDA can offer a novel perspective in economic data analysis, highlighting both stability and change within the inflationary process and suggesting the existence of recurring economic phenomena. Such insights could pave the way for further research integrating TDA with traditional economic models, potentially leading to a deeper understanding and more accurate predictions of economic trends.