Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation

Author

Emil Eirola
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

Gaussian Mixture Models Provide an Appealing Tool for Time Series Modelling. by Embedding the Time Series to a Higher-Dimensional Space, the Density of the Points Can Be Estimated by a Mixture Model. the Model Can Directly Be Used for Short-To-Medium Term Forecasting and Missing Value Imputation. the Modelling Setup Introduces Some Restrictions on the Mixture Model, Which When Appropriately Taken into Account Result in a More Accurate Model. Experiments on Time Series Forecasting Show that Including the Constraints in the Training Phase Particularly Reduces the Risk of overfitting in Challenging Situations with Missing Values or a Large Number of Gaussian Components. © 2013 Springer-Verlag.

Recommended Citation

E. Eirola and A. Lendasse, "Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8207 LNCS, pp. 162 - 173, Springer, Nov 2013.

The definitive version is available at https://doi.org/10.1007/978-3-642-41398-8_15

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Gaussian mixture model; missing data; time series

International Standard Book Number (ISBN)

978-364241397-1

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

11 Nov 2013