If we need to predict future values for, let's say oil price, we could model the price in terms of other variables (the number of rigs available, the number of companies investing in oil, and so on). These are usually referred to as cross-sectional models. 

In the 1970s, many economists realized that these models were ineffective for predicting future values, and a new approach was proposed. The idea was then to use the series' past  values to predict the future, in such a way that the model was chosen to mimic the temporal correlation structure as much as possible. For example, if a series had a strong serial correlation of order 1 (meaning that two consecutive values would usually be correlated), we would choose a model that generated strong autocorrelations of order 1. The new methodology was called time series analysis and it has been the dominant statistical tool for forecasting since.

All the series used in this chapter were obtained from Argentina's open data portal https://datos.gob.ar/series/api. It contains hundreds of time series regarding finance, production, and economy.