Social media data assisted inference with application to stock prediction

Hao He, Arun Subramanian, Sora Choi, Pramod Kumar Varshney, Thyagaraju Damarla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The access to the massive amount of social media data provides a unique opportunity to the signal processing community for extracting information that can be used to infer about unfolding events. It is desirable to investigate the convergence of sensor networks and social media in facilitating the data-to- decision making process and study how the two systems can complement each other for enhanced situational awareness. In this paper, we propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock price data of Apple Inc. for prediction, where the stock data serves as a surrogate for sensor data. Superior prediction performance is demonstrated, by taking the non-linear dependence among social media data and sensor data into consideration.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages1801-1805
Number of pages5
Volume2016-February
ISBN (Print)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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