stumpy_tools

Project done by:

Name	Email
Diego Sanz-Gadea Sánchez	d.sanz-gadea@alu.icai.comillas.edu

Time Series Analysis using STUMPY LIBRARY patterns detection through subsequences of a sequence. Computing the Euclidian Distances.

S.M. Law, (2019). STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining. Journal of Open Source Software, 4(39), 1504.

Use Example

Starting from a Time Series DataFrame df

Instanciating the class SimilarPattern

patterns=SimilarPattern(df,24)

nav.get_plot(continious=True)

• If the DataFrame inserted has several columns a stacked proccess computes the 2D array into 1D, pivoting the columns registers to only one column, increasing registers number. Otherwise the sequence is a simply one, so the pattern is not taking into account other variables. Just one, the given.
• The second argument indicates the period of the subsequence to be compute. For 2D (several column DataFrame). Hidely the subsequence distancees computes as 24*nº columns

Once created the SimilarPattern object, the methods arises. Firstly with .get_similar_patter(date)

patterns.get_similar_pattern('2023-01-26')

['2023-01-18']

model.fit( X_train, y_train, X_prev)

The most similar date subsequence is shown. Sometimes some dates cant compute similarities. It depends on the data.

pattern.plot_similar_pattern('2023-01-26')

Motifs are stored as an attribute when some method using date is computed previously

pattern.seek_motif

Also the overall matrix profile could be compute as:

pattern.get_matrix_profile()

Citations

S.M. Law, (2019). STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining. Journal of Open Source Software, 4(39), 1504.