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Time Series Forecasting by Recommendation: An Empirical Analysis on Amazon Marketplace

  • Álvaro Gómez-LosadaEmail author
  • Néstor Duch-Brown
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)

Abstract

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 (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.

Keywords

Collaborative Filtering Time series Forecasting Data science 

Notes

Disclaimer

The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Joint Research Centre, European CommissionSevilleSpain

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