Computational Performance of the Forecasting Algorithms

The computational performance has been central in the design of all the forecasting methods in the library.

This is because conventional wisdom wants the forecasting algorithms to be slow or limited in scope (or complex to use). This has been true in the past since the vast majority of players in the field designed dinosaur-era software that was indeed complex to use and incredibly slow. The computational power available only ten years ago was a fraction of what is available now and this is another reason why the algorithms have always been designed as simple (and poor) as possible: just to use less computational power!
Now the advent of modern microprocessors has given a chance to all to use modern algorithms that were unfeasible only few years ago.

The computational benchmarks are clearly indicating that even the most complex algorithms are really fast and can be used even in the most demanding real-life applications or where there are millions of forecasts that need to be produced like in the modern supply chains.

The benchmarks have been carried out on a 1.5GHz machine with 1G of memory (memory is used fractionally during the tests). The tests consist in running repeatedly the forecast algorithm on the same data series generated in a random fashion. The figure reported is the number of forecasts per second, so the higher the better.