Forecasting timeseries is a common problem in data science/machine learning. It asks, given a set of observations of the past, what the future will look like.
Some real world applications of timeseries forecasting include:
- Sales/demand forecasting: Say you're an ice cream chain. You might expect that sales will be much higher in the summer and lower in the winter, but trend higher year-over-year overall because you're investing in advertising. The sales forecasts would be useful for things like setting quota for your salespeople, financial disclosure/valuation, and inventory planning.
- Capacity planning: In a software context, capacity planning refers to ensuring enough compute resources to serve expected traffic. More broadly, capacity planning asks, how many servers, employees, meals, parking spaces, etc