EN
Wavelet decomposition for time series: Determining input model by using mRMR criterion
Abstract
Determining the level of decomposition and coefficients used as input in
the wavelet modeling for time series has become an interesting problem
in recent years. In this paper, the detail and scaling coefficients that
would be candidates of input determined based on the value of Mutual
Information. Coefficients generated through decomposition with Maximal Overlap Discrete Wavelet Transform (MODWT) were sorted by
Minimal Redundancy Maximal Relevance (mRMR) criteria, then they
were performed using an input modeling that had the largest value
of Mutual Information in order to obtain the predicted value and the
residual of the initial (unrestricted) model. Input was then added one
based on the ranking of mRMR. If additional input no longer produced
a significant decrease of the residual, then process was stopped and the
optimal model was obtained. This technique proposed was applied in
both generated random and financial time series data.
Keywords
References
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Details
Primary Language
English
Subjects
Statistics
Journal Section
Research Article
Publication Date
February 1, 2015
Submission Date
January 20, 2014
Acceptance Date
April 22, 2014
Published in Issue
Year 2015 Volume: 44 Number: 1
APA
Warsito, B., Subanar, S., & Abdurakhman, A. (2015). Wavelet decomposition for time series: Determining input model by using mRMR criterion. Hacettepe Journal of Mathematics and Statistics, 44(1), 229-238. https://izlik.org/JA46SG36JR
AMA
1.Warsito B, Subanar S, Abdurakhman A. Wavelet decomposition for time series: Determining input model by using mRMR criterion. Hacettepe Journal of Mathematics and Statistics. 2015;44(1):229-238. https://izlik.org/JA46SG36JR
Chicago
Warsito, Budi, Subanar Subanar, and Abdurakhman Abdurakhman. 2015. “Wavelet Decomposition for Time Series: Determining Input Model by Using MRMR Criterion”. Hacettepe Journal of Mathematics and Statistics 44 (1): 229-38. https://izlik.org/JA46SG36JR.
EndNote
Warsito B, Subanar S, Abdurakhman A (February 1, 2015) Wavelet decomposition for time series: Determining input model by using mRMR criterion. Hacettepe Journal of Mathematics and Statistics 44 1 229–238.
IEEE
[1]B. Warsito, S. Subanar, and A. Abdurakhman, “Wavelet decomposition for time series: Determining input model by using mRMR criterion”, Hacettepe Journal of Mathematics and Statistics, vol. 44, no. 1, pp. 229–238, Feb. 2015, [Online]. Available: https://izlik.org/JA46SG36JR
ISNAD
Warsito, Budi - Subanar, Subanar - Abdurakhman, Abdurakhman. “Wavelet Decomposition for Time Series: Determining Input Model by Using MRMR Criterion”. Hacettepe Journal of Mathematics and Statistics 44/1 (February 1, 2015): 229-238. https://izlik.org/JA46SG36JR.
JAMA
1.Warsito B, Subanar S, Abdurakhman A. Wavelet decomposition for time series: Determining input model by using mRMR criterion. Hacettepe Journal of Mathematics and Statistics. 2015;44:229–238.
MLA
Warsito, Budi, et al. “Wavelet Decomposition for Time Series: Determining Input Model by Using MRMR Criterion”. Hacettepe Journal of Mathematics and Statistics, vol. 44, no. 1, Feb. 2015, pp. 229-38, https://izlik.org/JA46SG36JR.
Vancouver
1.Budi Warsito, Subanar Subanar, Abdurakhman Abdurakhman. Wavelet decomposition for time series: Determining input model by using mRMR criterion. Hacettepe Journal of Mathematics and Statistics [Internet]. 2015 Feb. 1;44(1):229-38. Available from: https://izlik.org/JA46SG36JR