Paper Info
Title
Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction
Abstract
A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.
Year
DOI
Venue
2021
10.3390/s21217333
SENSORS
Keywords
DocType
Volume
time series segmentation, stationarity analysis, time series prediction improvement, size reduction in time series
Journal
21
Issue
ISSN
Citations 
21
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ricardo Petri Silva100.68
Bruno Bogaz Zarpelão29810.59
Alberto Cano313011.20
Sylvio Barbon44610.97