short-paper

A Study of Feature Construction for Text-based Forecasting of Time Series Variables

Authors:

Dominic Seyler,

Shubhra Kanti Karmaker Santu,

ChengXiang ZhaiAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 2347 - 2350

https://doi.org/10.1145/3132847.3133109

Published: 06 November 2017 Publication History

Abstract

Time series are ubiquitous in the world since they are used to measure various phenomena (e.g., temperature, spread of a virus, sales, etc.). Forecasting of time series is highly beneficial (and necessary) for optimizing decisions, yet is a very challenging problem; using only the historical values of the time series is often insufficient. In this paper, we study how to construct effective additional features based on related text data for time series forecasting. Besides the commonly used n-gram features, we propose a general strategy for constructing multiple topical features based on the topics discovered by a topic model. We evaluate feature effectiveness using a data set for predicting stock price changes where we constructed additional features from news text articles for stock market prediction. We found that: 1) Text-based features outperform time series-based features, suggesting the great promise of leveraging text data for improving time series forecasting. 2) Topic-based features are not very effective stand-alone, but they can further improve performance when added on top of n-gram features. 3) The best topic-based feature appears to be a long-term aggregation of topics over time with high weights on recent topics.

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Cited By

Praturi SRamakrishnan ADeepthi L(2024)Stock Price Prediction Using Sentiment Analysis on Financial NewsData Science and Applications10.1007/978-981-99-7817-5_40(551-567)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_40
Obst DClaudel SCugliari JGhattas BGoude YOppenheim G(2024)Textual data for electricity load forecastingQuality and Reliability Engineering International10.1002/qre.363740:8(4187-4208)Online publication date: 24-Aug-2024
https://doi.org/10.1002/qre.3637
Duong VDuc Nhan BBui MWang J(2023)Ensemble Learning Technique with A Novelty Multi‑Source Information for Stock Price MovementsProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3629007(707-714)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3629007
Show More Cited By

Index Terms

A Study of Feature Construction for Text-based Forecasting of Time Series Variables
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
      2. Document topic models

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cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Published: 06 November 2017

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November 6 - 10, 2017

Singapore, Singapore

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Praturi SRamakrishnan ADeepthi L(2024)Stock Price Prediction Using Sentiment Analysis on Financial NewsData Science and Applications10.1007/978-981-99-7817-5_40(551-567)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_40
Obst DClaudel SCugliari JGhattas BGoude YOppenheim G(2024)Textual data for electricity load forecastingQuality and Reliability Engineering International10.1002/qre.363740:8(4187-4208)Online publication date: 24-Aug-2024
https://doi.org/10.1002/qre.3637
Duong VDuc Nhan BBui MWang J(2023)Ensemble Learning Technique with A Novelty Multi‑Source Information for Stock Price MovementsProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3629007(707-714)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3629007
Karmaker (“Santu”) SHassan MSmith MXu LZhai CVeeramachaneni K(2021)AutoML to Date and Beyond: Challenges and OpportunitiesACM Computing Surveys10.1145/347091854:8(1-36)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3470918
Ranibaran GMoin MAlizadeh SKoochari A(2021)Analyzing effect of news polarity on stock market prediction: a machine learning approach2021 12th International Conference on Information and Knowledge Technology (IKT)10.1109/IKT54664.2021.9685403(102-106)Online publication date: 14-Dec-2021
https://doi.org/10.1109/IKT54664.2021.9685403
Seyler DShen JXiao JWang YZhai CBalog KSetty VLioma CLiu YZhang MBerberich K(2020)Leveraging Personalized Sentiment Lexicons for Sentiment AnalysisProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409850(109-112)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3409256.3409850
Seyler DLi LZhai CAlhajj R(2020)Semantic text analysis for detection of compromised accounts on social networksProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381432(417-424)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381432
Kuzi SCope WFerguson DGeigle CZhai C(2019)Automatic Assessment of Complex Assignments using Topic ModelsProceedings of the Sixth (2019) ACM Conference on Learning @ Scale10.1145/3330430.3333615(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3330430.3333615
Mohan SMullapudi SSammeta SVijayvergia PAnastasiu D(2019)Stock Price Prediction Using News Sentiment Analysis2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2019.00035(205-208)Online publication date: Apr-2019
https://doi.org/10.1109/BigDataService.2019.00035

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