Time Series Forecasting by Recommendation: An Empirical Analysis on Amazon Marketplace
This study proposes a forecasting methodology for univari ate 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 Collabo rative Filtering approach. A set of top-N values is recommended for this TS which represent the forec...
| Autores: | , |
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| Formato: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | Universidad Loyola Andalucía |
| Repositorio: | Brújula |
| OAI Identifier: | oai:repositorio.uloyola.es:20.500.12412/5446 |
| Acesso em linha: | https://hdl.handle.net/20.500.12412/5446 |
| Access Level: | acceso abierto |
| Palavra-chave: | Collaborative Filtering Time series Forecasting Data science |
| Resumo: | This study proposes a forecasting methodology for univari ate 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 Collabo rative 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 environ ment chosen to conduct forecasting (short length and unevenly spaced TS). 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. |
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