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The research of accuracy of searching cycles methods in symbolic sequences (time series) with presence of random noises.

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periodicity-research

The research of accuracy of searching cycles methods in symbolic sequences (time series) with presence of random noises.

Official topic of the work:

Research of Accuracy Cycles Identification Methods in Symbolic Sequences with Random Noises

Pugacheva, Korotkov algorithm

main proces of the algorithm:
  • based on mix of genetic algorithm and dynamic programming
  • works perfectly for small sizes of the periods
  • works normally (if you wanna get more concrete digits - ask about) with huge sequences
  • works with all types of noise
  • works with all types of periodicity

About all future analysis - percent (доля) in this case one (percent of noisy periods) * (percent of noise in period) where percent of noisy periods = percent of noisy periods in my tests

Brief analysis of the picture
  • just to show that algorithm (in my research) works more efficiently with small periods
main conces of the algorithm:
  • sometimes you can wait for hours to get result (if you use cluster)
  • not easy to prepare the customizations for genetic algorithm
  • not easy to make good first initialization of the organisms - matrices

Examples of Data files that was tested on cluster

  • examples maybe here If you wanna test Korotkov algortihms - see the cluster link

CONV by Mohamed G. Elfeky, Walid G. Aref, Senior Member, IEEE, and Ahmed K. Elmagarmid

main proses of the algorithm:
  • based on convolution
  • fast computational complexity due to FFT in the kernel
  • works well with any sizes of alphabet and any sizes of sequence
Brief analysis of the picture
  • just to show the efficiency (time complexity) of the CONV algorithm
main disadvantages of the algorithm:
  • does not work with any noises (works well only with replace noise)
  • there are no single implementation for all types of periodicity (exist separated implementations for partial and segment periodicity)
to test this algorithm see the "instruction to test algorithms"
  • additional implementation with GUI on java

WARP by Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid

main proses of the algorithm:
  • based on dynamic time warping
  • easy to understand, easy to implement
main cons of the algorithm:
  • does not work great with all types of noises

to test this algorithm see the "instruction to test algorithms"

  • additional implementation with GUI on java

STNR by Faraz Rasheed, Reda Alhajj

main feature of the algorithm:
  • based on suffix tree
  • works great for all types of periodicities
  • works well for all types of noise
main cons of the algorithm:
  • works well only for discrete small alphabet

to test this algorithm ask about additional implementation that based on masudio solution

instruction to test algorithms:
  • generate_data.py - customize and run the file
  • test.py choose the test function and run it
Brief analysis of the picture
  • just to show the results of my tests

Send me email to [email protected] if you want to compare these algorithms with any simple like algorithms below All of them were implemented on Java with GUI interface (JavaFX)

Available algorithms to compare with:

Floyd algorithm

pros:
  • easy to understand, easy to implement
cons:
  • no chances to detect difficult periodicity (more than one same elements in a period)
  • single cycle estimation

Brean algorithm

pros and cons are the same
  • modification of Floyd (binary degree representation) that estimate period more correct

Gosper algorithm

pros and cons are the same as Floyd
  • independent implementation

Nivash algorithm

pros and cons:
  • very fast
  • more correct than previous
  • was developed only in 2004

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The research of accuracy of searching cycles methods in symbolic sequences (time series) with presence of random noises.

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