Over the last few years we’ve looked at various tools to help us identify exploitable patterns in asset prices. In particular we have considered basic econometrics, statistical machine learning and Bayesian statistics.
While these are all great modern tools for data analysis, the vast majority of asset modeling in the industry still makes use of statistical time series analysis. In this article we are going to examine what time series analysis is, outline its scope and learn how we can apply the techniques to various frequencies of financial data.
What is Time Series Analysis?
Firstly, a time series is defined as some quantity that is measured sequentially in time over some interval.
In its broadest form, time series analysis is about inferring what has happened to a series of data points in the past and attempting to predict what will happen to it the future.
However, we are going to take a quantitative statistical approach to time series, by assuming that our time series are realisations of sequences of random variables. That is, we are going to assume that there is some underlying generating process for our time series based on one or more statistical distributions from which these variables are drawn…
Time series analysis attempts to understand the past and predict the future.