scholarly journals Correlating Time Series Signals and Event Logs in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7128
Author(s):  
Kazimierz Krosman ◽  
Janusz Sosnowski
Keyword(s):  
Time Series ◽  
Embedded Systems ◽  
Object Oriented ◽  
Real Data ◽  
Time Intervals ◽  
Event Logs ◽  
Time Offset ◽  
Correlation Process ◽  
Device Operation ◽  
Signal Time

In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

Object-oriented programming for embedded systems

1995 ◽  
Vol 30 (9) ◽  
pp. 3-8 ◽  
Author(s):  
Stuart Maclean ◽  
Sean Smith
Keyword(s):  
Embedded Systems ◽  
Object Oriented ◽  
Object Oriented Programming

Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

2021 ◽  
pp. 190-200
Author(s):  
Lesia Mochurad ◽  
Yaroslav Hladun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Real Data ◽  
Finger Tapping ◽  
Similar Distribution ◽  
Model Learning ◽  
Machine Learning Model ◽  
Finger Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.

scholarly journals Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽  
2018 ◽  
Vol 6 (7) ◽  
pp. 124 ◽  
Author(s):  
Elena Barton ◽  
Basad Al-Sarray ◽  
Stéphane Chrétien ◽  
Kavya Jagan
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Data Analysis ◽  
Real Data ◽  
Real Data Analysis ◽  
Convex Optimisation ◽  
Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

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