Chapter 3: What is Time Series?
Time series prediction involves collecting data on an event over time. That means the data is oscillating in a relatively continuous way. It is useful for predicting problems that don't have a linear progression with continuously growing or decreasing results. Some have season-dependent behavior. For example, the demand for inventory rotation items can change depending on the season of the year. Time series analysis could help to optimize the planning of purchase orders to avoid overstocking warehouses. The inventory in the warehouse needs rotation to keep the cash flow moving, pay the providers, and get more stock to sell. Another application is to analyze the passenger traffic in transportation in order to plan the seasonal allocation of the service equipment.
In the same way that linear regression models need statistical analysis to discover the relationships between the variables, time series models need data that has an autocorrelation....
