README.md

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

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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