Robust histogram-based feature engineering of time series data

Zdravevski, Eftim; Lameski, Petre; Mingov, Riste; Kulakov, Andrea; Gjorgjevikj, Dejan

doi:10.15439/2015f420

Cited by 30 publications

(22 citation statements)

References 14 publications

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“…They not only provided an insightful view on the state-of-the-art in multidimensional time series analysis but also contained inspiring new ideas, design specifically for the considered problem. The most interesting of these ideas are described by their authors in separate papers submitted for the competition track of AAIA'15 conference [2], [11], [15], [17], [19], [20].…”

Section: Summary Of the Competition Resultsmentioning

confidence: 99%

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Meina¹,

Janusz²,

Rykaczewski³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-In this paper, we summarize AAIA'15 data mining competition: Tagging Firefighter Activities at a Fire Scene, which was held between March 9 and July 6, 2015. We describe the scope and background of the competition. We also reveal details regarding the data set used in the competition, which was collected and tagged specifically for the purpose of this data challenge. We explain the data acquisition process which involved using a body sensor network system consisting of several inertial measurement units and a physiological data sensor. Finally, we briefly discuss submitted results with respect to their possible real-life application in our decision support system.

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Section: Summary Of the Competition Resultsmentioning

confidence: 99%

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Meina¹,

Janusz²,

Rykaczewski³

et al. 2015

Annals of Computer Science and Information Systems

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“…Another technique named histogram-based data clustering has been incorporated in different field of applications [50][51][52] to group similar data into clusters in order to reduce redundant data. The data points of each cluster have been assigned in a systematic way where the random initialization to determine centroids and iteration to converge individual cluster are not required.…”

Section: Data Clustering Techniquesmentioning

confidence: 99%

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Alam

Aziz

Latif

et al. 2020

Sensors

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

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“…A recent data mining competition for posture recognition of firefighters [2] inspired different feature engineering approaches that are very effective [3,4,5]. Using the proposed approaches there, from each series of readings the system generates the following types of features: basic statistics (minimum, maximum, range, arithmetic mean, harmonic mean, geometric mean, median, mode, standard deviation, variance, skewness, kurtosis, signal-to-noise ratio, energy, etc.…”

Section: A Feature Generatorsmentioning

confidence: 99%

“…Using the proposed approaches there, from each series of readings the system generates the following types of features: basic statistics (minimum, maximum, range, arithmetic mean, harmonic mean, geometric mean, median, mode, standard deviation, variance, skewness, kurtosis, signal-to-noise ratio, energy, etc. ); curve fitting parameters [4]; equal-width histogram features [5]; percentile based features (first quartile, median, third quartile, inter-quartile range, amplitude, etc.) [3]; auto-correlation of the signal with several types of correlations (signal processing auto-correlation and Pearson, Spearman and Kendall correlation) [4]; and intercorrelations between each pair of raw time series values using the aforementioned types of correlation coefficients.…”

Section: A Feature Generatorsmentioning

confidence: 99%

“…Authors of [3,4,5] demonstrated that this approach can further enhance the predictive performance of the system. Therefore, the framework uses the following time series generators (TSG): first derivatives of the original series [5]; amplitudes, frequencies and magnitudes obtained with fast Fourier transformation [3]; delta series (the deviations of the original values from the mean of the particular block of the time series), which can remove the seasonality in the data; sliding window time series [6]; and combining two time series by multiplying, subtracting or dividing their values [4].…”

Section: B Time Series Generatorsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Feature Engineering for Prediction of Dangerous Seismic Activities in Coal Mines

Zdravevski

Lameski

Kulakov

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require any manual tuning. Furthermore, we have analyzed the impact of overlapping of input data on model robustness. We argue that training an ensemble of classifiers with distinct (i.e. nonoverlapping) chronological data rather than one classifier with all available data can produce more reliable and robust prediction models. By doing that, we were able to avoid overfitting and obtain the same score performance on the evaluation and test datasets, despite the significant data drift in the datasets.

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Robust histogram-based feature engineering of time series data

Cited by 30 publications

References 14 publications

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks

Automatic Feature Engineering for Prediction of Dangerous Seismic Activities in Coal Mines

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