TODO:

* Patterns are learned using ensemble of regression trees. Approach is
  embarrassingly parallel. A combine routine that can combine multiple trees
  in an ensemble is required to be implemented to benefit from parallelism.
  
* The probability of each observation being used by the model is not 
  equal because of the sampling scheme. The observations towards the start
  and end of the time series are less likely to appear in the segments
  A fair sampling strategy is needed for segment selection.
  
* Indetifying common patterns and plotting them is needed for interpretability
  purposes.
  
* Currently the package requires UCR time series database format (each time 
  series is a row and columns are the observations over time). Therefore,
  time series are assumed to be the same length although LPS can handle
  time series of different length which requries changes in input format.
  
* LPS can work for multivariate time series similarity. Current version
  needs modifications for multivariate time series.
  
* LPS can work for categorical time series (i.e. DNA sequences). Current version
  requires modification for categorical variables.

* Regression trees in LPS can be used for prediction (to characterize the time 
  series). An estimate of error rate (either out-of-bag or over all series) can 
  be provided to determine the number of trees.
  
========================================================================
Change in 1.0-1:

* Fixed some minor problems with memory allocations.
