Similar to common regression methods, time series algorithms predict a numerical value. In contrast to common regression methods, time series predictions are focused on future values of an ordered series. These predictions are commonly called forecasts.
The time series algorithms are univariate algorithms. This means that the independent variable is a time column or an order column. The forecasts are based on past values. They are not based on other independent columns.
Time series algorithms are different from common regression algorithms because they do not only predict future values but also incorporate seasonal cycles into the forecast.
An example application is forecasting warehouse stock levels to optimize purchasing management.