Converting Time Series Data to Numeric Representations Using Alphabetic Mapping and k-mer strategy

TL;DR

This paper introduces a novel method to convert time series data into sequence-like representations using alphabetic mapping, enabling bioinformatics sequence analysis techniques to uncover patterns in complex data.

Contribution

It proposes a unique alphabetic mapping technique to transform time series into sequences, facilitating the application of bioinformatics sequence analysis methods.

Findings

Effective conversion of real-world time series into character sequences.

Enhanced pattern recognition through sequence classification.

New perspective on time series analysis using bioinformatics tools.

Abstract

In the realm of data analysis and bioinformatics, representing time series data in a manner akin to biological sequences offers a novel approach to leverage sequence analysis techniques. Transforming time series signals into molecular sequence-type representations allows us to enhance pattern recognition by applying sophisticated sequence analysis techniques (e.g. $k$-mers based representation) developed in bioinformatics, uncovering hidden patterns and relationships in complex, non-linear time series data. This paper proposes a method to transform time series signals into biological/molecular sequence-type representations using a unique alphabetic mapping technique. By generating 26 ranges corresponding to the 26 letters of the English alphabet, each value within the time series is mapped to a specific character based on its range. This conversion facilitates the application of sequence analysis algorithms, typically used in bioinformatics, to analyze time series data. We demonstrate the effectiveness of this approach by converting real-world time series signals into character sequences and performing sequence classification. The resulting sequences can be utilized for various sequence-based analysis techniques, offering a new perspective on time series data representation and analysis.