Work

tsfeaturex: An R Package for Automating Time Series Feature Extraction

Year: 2019

Type: article

Abstract: In today's digital world, data collection and storage costs are quite low.Humans are collectively outputting 2.5 quintillion bytes of data every day; by 2020, each person will generate ~1.7 MB every second (IBM Marketing Cloud, 2017).At this scale, intensive longitudinal data about humans' behavior facilitates new discovery about the patterning of thought and action and potentially better prediction and optimization of health and well-being.In raw form, the 2.5 quintillion bytes of data generated daily are noisy timeseries and difficult to interpret.Extraction of features from the time-series, however, allows:

Source: The Journal of Open Source Software

DOI: 10.21105/joss.01279

Authors: Nelson Roque, Nilam Ram

Institution: Pennsylvania State University

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Pennsylvania State University, National Institute on Aging

Award: Psychosocial Determinants and Biological Pathways to Healthy Aging (Pathways)