Nonlinearity Tests Using the Extrema of a Time Series

1998 ◽  
Vol 08 (09) ◽  
pp. 1831-1838 ◽  
Author(s):  
A. di Garbo ◽  
R. Balocchi ◽  
S. Chillemi
Keyword(s):  
Time Series ◽  
Real Time ◽  
Dynamical Behavior ◽  
Data Sets ◽  
Complex Solution ◽  
Time Axis ◽  
The Real ◽  
Starting Point ◽  
Analytical Properties ◽  
New Algorithms

The analytical properties of the solution of a system of ODEs in the complex time plane influence its dynamical behavior on the real time axis. In particular, the extrema of the real time solution can be associated to the singularities of the complex solution falling close to the real time axis. Moreover for a twice differentiable stochastic process, the expected value of the number of extrema for unit time can be determined. These two results are used here as the starting point to introduce two new algorithms to test for time series nonlinearity. They do not require the phase space reconstruction protocol and seem to work well also for short data sets.

scholarly journals Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3367 ◽  
Author(s):  
Nan Ding ◽  
Huanbo Gao ◽  
Hongyu Bu ◽  
Haoxuan Ma ◽  
Huaiwei Si
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Bayesian Network ◽  
Real Time ◽  
Multivariate Time Series ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Real ◽  
Hierarchical Temporal Memory ◽  
Sensing Time

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.

Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

2017 ◽  
Vol 10 (2) ◽  
pp. 130-144 ◽  
Author(s):  
Iwan Aang Soenandi ◽  
Taufik Djatna ◽  
Ani Suryani ◽  
Irzaman Irzaman
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Necessary Condition ◽  
Support Vector ◽  
Adaptive Selection ◽  
Content Type ◽  
The Real ◽  
Time Optimization ◽  
Starting Point ◽  
Real Time Optimization

Purpose The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor. Design/methodology/approach The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation. Findings This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy. Research limitations/implications In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system. Practical implications This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries. Originality/value The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

scholarly journals TW-SIR: Time-window based SIR for COVID-19 forecasts

2020 ◽  
Author(s):  
Zhifang Liao ◽  
Peng Lan ◽  
Zhingning Liao ◽  
Yan Zhang ◽  
Shengzong Liu
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Time Window ◽  
Data Sets ◽  
Machine Learning Method ◽  
The Real ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Time Changes ◽  
Epidemic Diseases

Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In order to reflect the real-time trend of the epidemic in the process of infection for different areas, different policies and different epidemic diseases, a general adapted time- window based SIR model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the Basic reproduction number R0 and the exponential growth rate of the epidemic. Multiple data sets of epidemic diseases are analyzed, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%

scholarly journals Speeding up eQTL scans in the BXD population using GPUs

2021 ◽  
Author(s):  
Chelsea Trotter ◽  
Hyeonju Kim ◽  
Gregory Farage ◽  
Pjotr Prins ◽  
Robert W Williams ◽  
...  
Keyword(s):  
Real Time ◽  
Matrix Multiplication ◽  
Reference Population ◽  
Mouse Strains ◽  
Data Sets ◽  
Whole Genome ◽  
Omics Data ◽  
Problem Size ◽  
Speed Advantage ◽  
New Algorithms

Abstract The BXD family of mouse strains are an important reference population for systems biology and genetics that have been fully sequenced and deeply phenotyped. To facilitate interactive use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enable near-real-time whole genome QTL scans for up to one million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, our method is more than 700 times faster than a R/qtl linear model genome scan using 16 threads. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (the number of cases, number of genotypes, and number of traits). Our approach is ideal for interactive web services, such as GeneNetwork.org that need to display results in real-time. Our implementation is available as the Julia language package LiteQTL at https://github.com/senresearch/LiteQTL.jl.

scholarly journals Has the Real-Time Reliability of Monthly Indicators Changed over Time?

2019 ◽  
pp. 1-7
Author(s):  
Mark Bognanni
Keyword(s):  
Time Series ◽  
Real Time ◽  
Economic Data ◽  
Short Term ◽  
Monthly Data ◽  
The Real ◽  
Data Release ◽  
Macroeconomic Indicators ◽  
Over Time

Economic data are routinely revised after they are initially released. I examine the extent to which the real-time reliability of six monthly macroeconomic indicators important to policymakers has remained stable over time by studying the time-series properties of their short-term and long-term revisions. I show that the revisions to many monthly economic indicators display systematic behaviors that policymakers could build into their real-time assessments. I also find that some indicators’ revision series have varied substantially over time, suggesting that these indicators may now be less useful in real time than they once were. Lastly, I find that substantial revisions tend to occur indefinitely after the initial data release, a result which suggests a certain degree of caution is in order when using even thrice-revised monthly data in policymaking.

scholarly journals Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 867
Author(s):  
Krisztina Kelevitz ◽  
Kristy F. Tiampo ◽  
Brianna D. Corsa
Keyword(s):  
Time Series ◽  
Real Time ◽  
Early Warning ◽  
Atmospheric Correction ◽  
Warning System ◽  
Streaming Data ◽  
Test Case ◽  
Data Sets ◽  
Volcanic Area ◽  
Gravity Measurements

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.

scholarly journals Real-time interpolation of streaming data

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Roman Dębski
Keyword(s):  
Time Series ◽  
Finite Difference ◽  
Real Time ◽  
Performance Measures ◽  
Spline Interpolation ◽  
Streaming Data ◽  
Data Sets ◽  
Look Ahead ◽  
Different Types ◽  
Hermite Splines

One of the key elements of real-time $C^1$-continuous cubic spline interpolation of streaming data is an estimator of the first derivative of the interpolated function that is more accurate than the ones based on finite difference schemas.Two such greedy look-ahead heuristic estimators (denoted as MinBE and MinAJ2) based on Calculus of Variations are formally defined (in closed form) together with the corresponding cubic splines they generate, and then comparatively evaluated in a series of numerical experiments involving different types of performance measures. The results presented show that the cubic Hermite splines generated by heuristic MinAJ2 significantly outperformed these based on finite difference schemas in terms of all tested performance measures (including convergence).The proposed approach is quite general. It can be directly applied to streams of univariate functional data like time-series. Multidimensional curves defined parametrically, after splitting, can be handled as well. The streaming character of the algorithm means that it can also be useful in processing data sets that are too large to fit in memory (e.g., edge computing devices, embedded time-series databases).

scholarly journals Computational capability of ecological dynamics

2021 ◽  
Author(s):  
Masayuki Ushio ◽  
Kazufumi Watanabe ◽  
Yasuhiro Fukuda ◽  
Yuji Tokudome ◽  
Kohei Nakajima
Keyword(s):  
Time Series ◽  
Real Time ◽  
Reservoir Computing ◽  
Computational Resource ◽  
Ecological Network ◽  
Medium Temperature ◽  
Ecological Dynamics ◽  
The Real ◽  
Computational Capability ◽  
Near Future

Ecological dynamics is driven by an ecological network consisting of complex interactions. Information processing capability of artificial networks has been exploited as a computational resource, yet whether an ecological network possesses a computational capability and how we can exploit it remain unclear. Here, we show that ecological dynamics can be exploited as a computational resource. We call this approach "Ecological Reservoir Computing" (ERC) and developed two types of ERC. In silico ERC reconstructs ecological dynamics from empirical time series and uses simulated system responses as reservoir states, which predicts near future of chaotic dynamics and emulates nonlinear dynamics. The real-time ERC uses population dynamics of a unicellular organism, Tetrahymena thermophila. Medium temperature is an input signal and changes in population abundance are reservoir states. Intriguingly, the real-time ERC has necessary conditions for reservoir computing and is able to make near future predictions of model and empirical time series.

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