Modeling time series data with ARMA

        
Time series, as the name suggests, tracks a value over a sequence of distinct time intervals. They are particularly important in the finance industry, where stock values are tracked over time and used to make predictions – known as forecasting – of the value at some future time. Good predictions coming from such data can be used to make better investments. Time series also appear in many other common situations, such as weather monitoring, medicine, and any places where data is derived from sensors over time.
        
Time series, unlike other types of data, do not usually have independent data points. This means that the methods that we use for modeling independent data will not be particularly effective. Thus, we need to use alternative techniques to model data with this property. There are two ways in which a value in a time series can depend on previous values. The first is where there is a direct relationship between the value...