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Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics

Publication ,  Conference
Ding, J; Noshad, M; Tarokh, V
Published in: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
January 29, 2016

Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique based on Gap statistics to learn the appropriate number of AR filters needed to model a time series. We define a new distance measure between stable AR filters and draw a reference curve that is used to measure how much adding a new AR filter improves the performance of the model, and then choose the number of AR filters that has the maximum gap with the reference curve. To that end, we propose a new method in order to generate uniform random stable AR filters in root domain. Numerical results are provided demonstrating the performance of the proposed approach.

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Published In

Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

DOI

ISBN

9781467384926

Publication Date

January 29, 2016

Start / End Page

1441 / 1446
 

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Ding, J., Noshad, M., & Tarokh, V. (2016). Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. In Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 1441–1446). https://doi.org/10.1109/ICDMW.2015.209
Ding, J., M. Noshad, and V. Tarokh. “Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics.” In Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 1441–46, 2016. https://doi.org/10.1109/ICDMW.2015.209.
Ding J, Noshad M, Tarokh V. Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. In: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. 2016. p. 1441–6.
Ding, J., et al. “Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics.” Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 2016, pp. 1441–46. Scopus, doi:10.1109/ICDMW.2015.209.
Ding J, Noshad M, Tarokh V. Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. 2016. p. 1441–1446.

Published In

Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

DOI

ISBN

9781467384926

Publication Date

January 29, 2016

Start / End Page

1441 / 1446