Article

F4: large-scale automated forecasting using fractals

Authors:

Deepayan Chakrabarti,

Christos FaloutsosAuthors Info & Claims

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

Pages 2 - 9

https://doi.org/10.1145/584792.584797

Published: 04 November 2002 Publication History

Abstract

Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic as well as chaotic time series. We use the technique of delay coordinate embedding, which needs several parameters; our contribution is the automated way of setting these parameters, using the concept of `intrinsic dimensionality'. Our operational system has fast and scalable algorithms for preprocessing and, using R-trees, also has fast methods for forecasting. The result of this work is a black-box which, given a time series as input, finds the best parameter settings, and generates a prediction system. Tests on real and synthetic data show that our system achieves low error, while it can handle arbitrarily large datasets.

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Cited By

Malishevsky A(2023)Improving Predictive Models in the Financial Sector Using Fractal AnalysisSystem Analysis and Artificial Intelligence10.1007/978-3-031-37450-0_7(117-132)Online publication date: 29-Aug-2023
https://doi.org/10.1007/978-3-031-37450-0_7
Meisenbacher STurowski MPhipps KRätz MMüller DHagenmeyer VMikut R(2022)Review of automated time series forecasting pipelinesWIREs Data Mining and Knowledge Discovery10.1002/widm.147512:6Online publication date: 9-Aug-2022
https://doi.org/10.1002/widm.1475
Do TMatsubara YSakurai Y(2020)Real-time Forecasting of Non-linear Competing Online ActivitiesJournal of Information Processing10.2197/ipsjjip.28.33328(333-342)Online publication date: 2020
https://doi.org/10.2197/ipsjjip.28.333
Show More Cited By

Index Terms

F4: large-scale automated forecasting using fractals
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management

November 2002

704 pages

ISBN:1581134924

DOI:10.1145/584792

General Chair:
Charles Nicholas
University of Maryland Baltimore County
,
Program Chairs:
David Grossman
Illinois Institute of Technology
,
Konstantinos Kalpakis
University of Maryland Baltimore County
,
Sajda Qureshi
Erasmus University, Rotterdam
,
Han van Dissel
Erasmus University, Rotterdam
,
Len Seligman
The MITRE Corporation

Copyright © 2002 ACM.

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Published: 04 November 2002

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CIKM02: Eleventh ACM International Conference on Information and Knowledge Management

November 4 - 9, 2002

Virginia, McLean, USA

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Cited By

Malishevsky A(2023)Improving Predictive Models in the Financial Sector Using Fractal AnalysisSystem Analysis and Artificial Intelligence10.1007/978-3-031-37450-0_7(117-132)Online publication date: 29-Aug-2023
https://doi.org/10.1007/978-3-031-37450-0_7
Meisenbacher STurowski MPhipps KRätz MMüller DHagenmeyer VMikut R(2022)Review of automated time series forecasting pipelinesWIREs Data Mining and Knowledge Discovery10.1002/widm.147512:6Online publication date: 9-Aug-2022
https://doi.org/10.1002/widm.1475
Do TMatsubara YSakurai Y(2020)Real-time Forecasting of Non-linear Competing Online ActivitiesJournal of Information Processing10.2197/ipsjjip.28.33328(333-342)Online publication date: 2020
https://doi.org/10.2197/ipsjjip.28.333
Jeffrey BAanensen DCroucher NBhatt S(2020)Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modellingWellcome Open Research10.12688/wellcomeopenres.16153.15(194)Online publication date: 19-Aug-2020
https://doi.org/10.12688/wellcomeopenres.16153.1
Matsubara YSakurai YTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Dynamic Modeling and Forecasting of Time-evolving Data StreamsProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330947(458-468)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330947
Matsubara YSakurai YPrakash BLi LFaloutsos C(2017)Nonlinear Dynamics of Information Diffusion in Social NetworksACM Transactions on the Web10.1145/305774111:2(1-40)Online publication date: 24-Apr-2017
https://dl.acm.org/doi/10.1145/3057741
Matsubara YSakurai YKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Regime Shifts in StreamsProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939755(1045-1054)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939755
Sakurai YMatsubara YFaloutsos CBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Mining Big Time-series Data on the WebProceedings of the 25th International Conference Companion on World Wide Web10.1145/2872518.2891061(1029-1032)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872518.2891061
Fraideinberze ARodrigues JCordeiro R(2016)Effective and Unsupervised Fractal-Based Feature Selection for Very Large Datasets: Removing Linear and Non-linear Attribute Correlations2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0093(615-622)Online publication date: Dec-2016
https://doi.org/10.1109/ICDMW.2016.0093
Wang SYan ZHu XYu PLi ZWang B(2016)CPBKnowledge and Information Systems10.1007/s10115-015-0899-349:1(243-271)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s10115-015-0899-3
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