Forecasting with Big Data: A Review

Authors: Hassani, H. and Silva, E.S.

Journal: Annals of Data Science

Volume: 2

Issue: 1

Pages: 5-19

Publisher: Springer

eISSN: 2198-5812

ISSN: 2198-5804

DOI: 10.1007/s40745-015-0029-9

Abstract:

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data.

http://eprints.bournemouth.ac.uk/21835/

http://www.springer.com/

Source: Manual

Preferred by: Emmanuel Sirimal Silva

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