research-article

Semi-supervised learning with data calibration for long-term time series forecasting

Authors:

Pang-Ning TanAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 133 - 141

https://doi.org/10.1145/1401890.1401911

Published: 24 August 2008 Publication History

Abstract

Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one prediction step to the next step. Yet, there is a broad range of applications such as climate impact assessments and urban growth planning that require long term forecasting capabilities for strategic decision making. Training an accurate model that produces reliable long term predictions would require an extensive amount of historical data, which are either unavailable or expensive to acquire. For some of these domains, there are alternative ways to generate potential scenarios for the future using computer-driven simulation models, such as global climate and traffic demand models. However, the data generated by these models are currently utilized in a supervised learning setting, where a predictive model trained on past observations is used to estimate the future values. In this paper, we present a semi-supervised learning framework for long-term time series forecasting based on Hidden Markov Model Regression. A covariance alignment method is also developed to deal with the issue of inconsistencies between historical and model simulation data. We evaluated our approach on data sets from a variety of domains, including climate modeling. Our experimental results demonstrate the efficacy of the approach compared to other supervised learning methods for long-term time series forecasting.

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Cited By

Parmezan ASouza VBatista G(2022)Time Series Prediction via Similarity Search: Exploring Invariances, Distance Measures and Ensemble FunctionsIEEE Access10.1109/ACCESS.2022.319284910(78022-78043)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3192849
Masum SLiu YChiverton J(2018)Multi-step Time Series Forecasting of Electric Load Using Machine Learning ModelsArtificial Intelligence and Soft Computing10.1007/978-3-319-91253-0_15(148-159)Online publication date: 11-May-2018
https://doi.org/10.1007/978-3-319-91253-0_15
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https://doi.org/10.3390/s16101629
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Index Terms

Semi-supervised learning with data calibration for long-term time series forecasting
1. Information systems

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cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

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Published: 24 August 2008

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August 24 - 27, 2008

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Parmezan ASouza VBatista G(2022)Time Series Prediction via Similarity Search: Exploring Invariances, Distance Measures and Ensemble FunctionsIEEE Access10.1109/ACCESS.2022.319284910(78022-78043)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3192849
Masum SLiu YChiverton J(2018)Multi-step Time Series Forecasting of Electric Load Using Machine Learning ModelsArtificial Intelligence and Soft Computing10.1007/978-3-319-91253-0_15(148-159)Online publication date: 11-May-2018
https://doi.org/10.1007/978-3-319-91253-0_15
Zwartjes AHavinga PSmit GHurink J(2016)QUEST: Eliminating Online Supervised Learning for Efficient Classification AlgorithmsSensors10.3390/s1610162916:10(1629)Online publication date: 1-Oct-2016
https://doi.org/10.3390/s16101629
Deng DShahabi CDemiryurek UZhu LYu RLiu YKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Latent Space Model for Road Networks to Predict Time-Varying TrafficProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939860(1525-1534)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939860
Tan BZhong EXiang EYang Q(2014)Multi-transferStatistical Analysis and Data Mining10.1002/sam.112267:4(282-293)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1002/sam.11226
Seshadhri CPinar AKolda T(2014)Wedge sampling for computing clustering coefficients and triangle counts on large graphsStatistical Analysis and Data Mining10.1002/sam.112247:4(294-307)Online publication date: 1-Aug-2014
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Deng HHan JLi HJi HWang HLu Y(2014)Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networksStatistical Analysis and Data Mining10.1002/sam.112237:4(308-321)Online publication date: 1-Aug-2014
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Abraham ZTan PPerdinan Winkler JZhong SLiszewska M(2014)Contour regressionStatistical Analysis and Data Mining10.1002/sam.112227:4(272-281)Online publication date: 1-Aug-2014
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Curtin RRam P(2014)Dual-tree fast exact max-kernel searchStatistical Analysis and Data Mining10.1002/sam.112187:4(229-253)Online publication date: 1-Aug-2014
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Ge LGao JNgo HLi KZhang A(2014)On handling negative transfer and imbalanced distributions in multiple source transfer learningStatistical Analysis and Data Mining10.1002/sam.112177:4(254-271)Online publication date: 1-Aug-2014
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