Learning Optimal Time Series Combination and Pre-Processing by Smart Joins

Abstract

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

“…In the 1990s, Shen et al proposed the trended fluctuation analysis method. Gil et al put forward the multifractal detrended fluctuation analysis (MF-DFA) method based on Shen et al, which could easily calculate the main parameters of time series [21,22].…”

Machine Learning Methods with Noisy, Incomplete or Small Datasets

“…The algorithm considers learning from strongly and weakly labeled data. On the other side, in [10], Gil et al (Spain) investigated the use of optimization in the preprocessing step of time series joining. More specifically, the authors proposed an error function to measure the adequateness of the joining and demonstrated the effectiveness of the proposed method on the synthetical datasets and real industrial process scenario.…”