Survey: Time-Series Data Preprocessing: A Survey and an Empirical Analysis
Journal article, 2024

Data are naturally collected in their raw state and must undergo a series of preprocessing steps to obtain data in their input state for Artificial Intelligence (AI) and other applications. The data preprocessing phase is not only necessary to fit input requirements but also effective in improving AI training efficiency and output accuracy. Data preprocessing is a time consuming and complex phase that lacks a unified and structured approach. We survey data preprocessing techniques under different categories to provide an extended and structured scope of data preprocessing relevant to numerical time-series data. We also provide an empirical analysis of the impact of preprocessing techniques on the quality of the data and on the performance of AI algorithms. In addition, we discuss the feasibility of distributing some of the surveyed techniques to the edge. Leveraging edge computing to distribute data preprocessing reduces the workload on central systems, creates more manageable data lakes, reduces the consumption of resources (e.g., energy) and enables EdgeAI.

Data Preprocessing

Data Quality

Author

Amal Tawalkuli

University of Luxembourg

Bastian Havers

Network and Systems

Vincenzo Massimiliano Gulisano

Network and Systems

Daniel Kaiser

University of Luxembourg

Thomas Engel

University of Luxembourg

Journal of Engineering Research

2307-1877 (ISSN) 2307-1885 (eISSN)

AUTOSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2

VINNOVA (2019-05884), 2020-03-12 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Computer Science

DOI

10.1016/j.jer.2024.02.018

More information

Latest update

3/27/2024