ABSTRACT
Time series analysis encapsulates the methods used to understand the sequence of data points and extract useful information from it. A main goal is that of forecasting successive values of the series. A typical assumption made is that there is some structure in the time series data. This structure may be somewhat obfuscated by random noise. Since time is an important part of a time series, let the user take a look at some data that contains time as one of its columns. A similar computation can be carried out for the bitcoin dataset. A partial autocorrelation function correlogram with a large spike at one lag that decreases after a few lags usually indicates that there is a moving average term in the series. In this case, the autocorrelation function will help the user to determine the order of the moving average term.
