• Slide to briefly explain our idea including all figures in higher resolution.

Donoho-Johnstone Benchmark

• This well-known benchmark is created ten years ago and cited by many publications. 
• You can download this benchmark at ftp://ftp.sas.com/pub/neural/dojo/dojo.html.
• We also provide the mirror of the time series in txt file here. Note that this file is required to run the codes for figure 9, 10, and 11.
• Source code for generating the result in figure 9.   Matlab figure behind figure 9. (Find an intrinsic dimensionality)
• Source code for generating the result in figure 10. Matlab figure behind figure 10. (Find an intrinsic cardinality)
• Source code for generating the result in figure 11. Matlab figure behind figure 11. (Compare among three models)

Physiology Dataset

• Above time series is an excerpt data from the Muscle dataset in [1].
• Original data is in the author's webpage; we also provided the mirror of the data here.
• Source code for generating the result in figure 13. 
• Matlab figure to show the result in figure 13.
  

Astronomy Dataset

• Six of 5,327 star light curves time series in class RRL annotated anomalies by human from [2].
• The mirror of the dataset with labels can be downloaded here.
  

Cardiology Dataset 

• A cardiology data from PhysioBank ATM of the MIT BIH Arrhythmia dataset, record 108, signal MLII.
• The mirror of the timeseries can be downloaded here.

• The time series are easily generated by step function with noise. Time series of six steps is shown in figure above.
• We compare our method with L-method; The comparison result shows that our method can find the correct dimensionality in every case. 
• We tested on 2-step time series to 16-step time series. These are the results for our method and L-method.