This study proposes a forecasting methodology for univariate time series (TS) using a Recommender System (RS). The RS is built from a given TS as only input data and following an item-based Collaborative Filtering approach. A set of top-N values is recommended for this TS which represent the forecasts. The idea is to emulate RS elements (the users, items and ratings triple) from the TS. Two TS obtained from Italy's Amazon webpage were used to evaluate this methodology and very promising performance results were obtained, even the difficult environment chosen to conduct forecasting. This performance is dependent on the similarity measure used and suffers from the same problems that other RSs (e.g., cold-start). However, this approach does not require high computational power to perform and its intuitive conception allows for being deployed with any programming language.

GOMEZ LOSADA Alvaro;  DUCH BROWN Nestor; 

2020-01-16

SPRINGER VERLAG

JRC115331

1865-1348 (online),   

https://link.springer.com/chapter/10.1007%2F978-3-030-20485-3_4,    https://publications.jrc.ec.europa.eu/repository/handle/JRC115331,   

10.1007/978-3-030-20485-3_4 (online),