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:
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tsfeaturex: An R Package for Automating Time Series Feature Extraction
Semantic Scholar · Computer Science · 2019
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: