How to manage multiple asset time series data with a diversity of time frequency inflows

Forecasting and understanding past movements in asset prices as well as other financial data has always been a key feature in determining the success of all kinds of investors. However, to conduct this in a fashion that is both sophisticated yet reliable can often be a major challenge to financial analysts due to a wide range of unique factors associated with data from the financial markets. One of these specific issues is that the rate at which data is received for a given asset will vary across many assets due to the nature of how stock prices change through time. This can make comparisons of fundamental features a difficult task and often results in conclusions that can be misleading. This project seeks to propose and discuss methods to manage this data such that robust comparisons can be made and hence assist analysts with the role of drawing well-founded conclusions from the information available.