Robust histogram-based feature engineering of time series data
Date Issued
2015-09-13
Author(s)
Mingov, Riste
Gjorgjevikj, Dejan
Abstract
Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and
analyzed is financial data and sensor readings. Various businesses
have realized that financial time series analysis is a powerful
analytical tool that can lead to competitive advantages. Likewise,
sensor networks generate time series and if they are properly
analyzed can give a better understanding of the processes that
are being monitored. In this paper we propose a novel generic
histogram-based method for feature engineering of time series
data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with
first derivatives, and finally calculating histograms. By doing all
of those steps the goal is three-fold: achieve invariance to different
factors, good modeling of the data and preform significant feature
reduction. This method was applied to the AAIA Data Mining
Competition 2015, which was concerned with recognition of
activities carried out by firefighters by analyzing body sensor
network readings. By doing that we were able to score the third
place with predictive accuracy of about 83%, which was about
1% worse than the winning solution.
analyzed is financial data and sensor readings. Various businesses
have realized that financial time series analysis is a powerful
analytical tool that can lead to competitive advantages. Likewise,
sensor networks generate time series and if they are properly
analyzed can give a better understanding of the processes that
are being monitored. In this paper we propose a novel generic
histogram-based method for feature engineering of time series
data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with
first derivatives, and finally calculating histograms. By doing all
of those steps the goal is three-fold: achieve invariance to different
factors, good modeling of the data and preform significant feature
reduction. This method was applied to the AAIA Data Mining
Competition 2015, which was concerned with recognition of
activities carried out by firefighters by analyzing body sensor
network readings. By doing that we were able to score the third
place with predictive accuracy of about 83%, which was about
1% worse than the winning solution.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
Robust_histogram-based_feature_engineeri20160826-6174-qmlljm-with-cover-page-v2.pdf
Size
447.56 KB
Format
Adobe PDF
Checksum
(MD5):009b73e30ee4e3d82e3d64df44ca4cf0