Due to the continuous and chronologically ordered nature of time series data, there is a likelihood that there will be some degree of correlation between the series observations. For instance, the temperature in the next hour is not a random event since, in most cases, it has a strong relationship with the current temperature or the temperatures that have occurred during the past 24 hours. In many cases, the series of past observations contains predictive information about future events, which can be utilized to forecast the series' future observations. Throughout this chapter, we will focus on identifying and revealing those relationships with the use of correlation analysis techniques, such as the autocorrelation and cross-correlation functions, along with the data visualization tools.
This chapter will cover the following topics:
- Causality versus...
