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When performance of a time series-based system needs to be improved, it is desirable to consider other time series representations and comparison methods.
In the paper, we introduced a hierarchical Bayesian statespace model to estimate time delay among multiple time series.
The user can overlay multiple time series for visual comparison and can download measurements.
VAR is a statistical model that captures the linear interdependencies among multiple time series.
For studies with multiple time series sampling, increasing weight was given to later time-point samples.
To our knowledge, Wigwams is the only algorithm capable of mining multiple time series (on varying time scales) for dependent co-expression across subsets of the time series.
We also propose a spline interpolation method to combine results from multiple time series data.
In this work, a simple yet effective bag-of-words representation that is originally developed for text document analysis is extended for biomedical time series representation.
SN and TSI data sets have a time series representation with summarized monthly observations.
Second, we combined graphical and analytical methods to determine the specification of the model based on the best time series representation of each series.
Geweke, J. Measurement of linear-dependence and feedback between multiple time-series.
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