scholarly journals Automated Data-Driven Approach for Gap Filling in the Time Series Using Evolutionary Learning

2021 ◽  
pp. 633-642
Author(s):  
Mikhail Sarafanov ◽  
Nikolay O. Nikitin ◽  
Anna V. Kalyuzhnaya
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Evolutionary Learning ◽  
Gap Filling ◽  
Data Driven Approach

scholarly journals Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach

2018 ◽  
Vol 28 (7) ◽  
pp. 075502 ◽  
Author(s):  
Francisco Traversaro ◽  
Francisco O. Redelico ◽  
Marcelo R. Risk ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach

A data-driven approach for denoising GNSS position time series

2017 ◽  
Vol 92 (8) ◽  
pp. 905-922 ◽  
Author(s):  
Yanyan Li ◽  
Caijun Xu ◽  
Lei Yi ◽  
Rongxin Fang
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach ◽  
Position Time Series ◽  
Gnss Position Time Series

scholarly journals Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

2021 ◽  
Vol 33 (7) ◽  
pp. 077105
Author(s):  
Tomoki Inoue ◽  
Yu Matsuda ◽  
Tsubasa Ikami ◽  
Taku Nonomura ◽  
Yasuhiro Egami ◽  
...  
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Pressure Sensitive Paint ◽  
Modal Expansion ◽  
Pressure Sensitive ◽  
Data Driven Approach

Data-driven approach to identify obsolete functions of products for design improvements

2021 ◽  
pp. 1-14
Author(s):  
Zhihua Zhao ◽  
Yupeng Li ◽  
Xuening Chu
Keyword(s):  
Time Series ◽  
Functional Performance ◽  
Smart Phone ◽  
Data Driven ◽  
Design Elements ◽  
Customer Expectations ◽  
Data Driven Approach ◽  
Identification Approach ◽  
Previous Identification

Identifying defective design elements is a prerequisite for design improvements. Previous identification methods were implemented in the context of static customer requirements (CRs). However, CRs always evolve continuously, which easily leads to a failure of existing product functions in fulfilling customer expectations; this, in turn, can lead to a decline in customer satisfaction. In this study, the phenomenon is termed as ‘function obsolescence’, and a data-driven identification approach for obsolete functions is proposed for design improvements. Firstly, product operating data are employed to construct the observing parameters of functional performance (OPs), and based on the distribution of OPs, the desired level of functional performance (DL) is defined to quantitatively characterise CRs. Secondly, the time series of DL is constructed to embody the evolution of CRs, in which a Sigmoid-like function is employed to establish a dissatisfaction function. With the time series, an obsolescence index measuring the severity of obsolescence for each function is defined to identify obsolete functions. A case study was implemented on a smart phone to identify its obsolete functions to demonstrate the effectiveness of the proposed methodology. The results show that some potentially obsolete functions can be identified by the proposed method considering the evolution of CRs.

A non-parametric, data-driven approach to despiking ocean surface wave time series

2021 ◽  
Author(s):  
Mika P. Malila ◽  
Patrik Bohlinger ◽  
Susanne Støle-Hentschel ◽  
Øyvind Breivik ◽  
Gaute Hope ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Wave ◽  
Missing Values ◽  
Computational Cost ◽  
Ocean Surface ◽  
Data Driven ◽  
Storm Event ◽  
Wide Range ◽  
Data Driven Approach ◽  
Ocean Surface Wave

AbstractWe propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian Process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space-based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four co-located 5-Hz laser altimeters during a much-studied storm event the North Sea covering a wide range of sea states.

scholarly journals Stability Monitoring of Rotorcraft Systems: A Dynamic Data-Driven Approach

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Siddharth Sonti ◽  
Eric Keller ◽  
Joseph Horn ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Data Driven ◽  
Series Data ◽  
Dynamic Mode ◽  
Dynamic Data ◽  
Stability Monitoring ◽  
Data Driven Approach

This brief paper proposes a dynamic data-driven method for stability monitoring of rotorcraft systems, where the underlying concept is built upon the principles of symbolic dynamics. The stability monitoring algorithm involves wavelet-packet-based preprocessing to remove spurious disturbances and to improve the signal-to-noise ratio (SNR) of the sensor time series. A quantified measure, called Instability Measure, is constructed from the processed time series data to obtain an estimate of the relative instability of the dynamic modes of interest on the rotorcraft system. The efficacy of the proposed method has been established with numerical simulations where correlations between the instability measure and the damping parameter(s) of selected dynamic mode(s) of the rotor blade are established.

scholarly journals New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 568 ◽  
Author(s):  
Minseok Kang ◽  
Kazuhito Ichii ◽  
Joon Kim ◽  
Yohana M. Indrawati ◽  
Juhan Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Data Driven ◽  
Series Data ◽  
Gap Filling ◽  
Long Period ◽  
Flux Data ◽  
Data Gaps ◽  
Data Driven Approach

In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach.

A data-driven approach to complex ecological systems

2020 ◽  
pp. 116-133
Author(s):  
Michio Kondoh ◽  
Kazutaka Kawatsu ◽  
Yutaka Osada ◽  
Masayuki Ushio
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Dynamic Properties ◽  
Spatial Scales ◽  
Interspecific Interaction ◽  
Community Level ◽  
Data Driven ◽  
Ecological Communities ◽  
The Real ◽  
Data Driven Approach

Interspecific interaction has been a key concept in ecology to understand the structure and dynamics of ecological communities. Important, yet often overlooked, is that an interspecific interaction is a product of multiple biological processes at various temporal and spatial scales, including changes in demographic parameters such as birth and death rates, behavioral responses such as inter-habitat movements, and hiding and evolutionary responses in a longer temporal scale. Each of those mechanisms, according to ecological theory, potentially affects population dynamics and modifies the community-level properties such as community complexity and stability in different manners. Here, a question arises: how does the net interspecific interaction, which is made up with those multiple processes, look like in the real nature? How do changes depend on the temporal or spatial scale? In this chapter we show that a data-driven approach using demographic time series is a powerful tool to answer those questions. According to nonlinear dynamics theory, a time series of a variable contains information about the dynamic system that the variable belongs to. We can use this fact to identify interspecific interactions, quantify their signs and strengths and evaluate its effect to community-level dynamic properties. Some results we got by applying the time-series analysis based on nonlinear dynamics theory (called Empirical Dynamic Modeling) to empirical demographic data, experimental or observational, will be presented, which will demonstrate how fluctuating and condition-dependent the real interactions are and reveal how those interactions give rise to the dynamic properties at higher organization levels.

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