Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
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Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Selecting hidden Markov model state number with cross-validated likelihood
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multistep-Ahead time series prediction
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Learning dynamic temporal graphs for oil-production equipment monitoring system
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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.