Abstract
It is well known that time series analysis is the method to build a model and to make statistical inference based on historical records. Therefore the model will be dependent on the choice of sample data and the modeling method. Owing to the complexity and uncertainty of practical problems, the characteristics of time series will probably be variant in coming periods. So in order to make a proper prediction, improving the adaptability of the model is necessary.
The paper considers the method of improving the adaptability of time series models based on introducing dynamic innovation mechanism to the model. Firstly, the time series model is translated into the form of state space, and the controllability of the model is analyzed. Secondly, based on the error feedback principle, an error innovation term is introduced into the time-series model to improve the adaptability of the model. The theoretical result is obtained. Finally, some case studies are shown to demonstrate the efficiency of the method.
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Wang, W. (2012). The Method on Improving the Adaptability of Time Series Models Based on Dynamical Innovation. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31965-5_25
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DOI: https://doi.org/10.1007/978-3-642-31965-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31964-8
Online ISBN: 978-3-642-31965-5
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