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.

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.

scholarly journals Monitoring the evolution of the COVID-19 pandemic in China, South Korea, Italy and USA through the net relative rate of infection of the total number of confirmed cases

2020 ◽  
Author(s):  
Joao Manoel Losada Moreira
Keyword(s):  
Time Series ◽  
South Korea ◽  
Real Time ◽  
Doubling Time ◽  
Temporal Evolution ◽  
Relative Rate ◽  
Rate Of Change ◽  
Spread Rate ◽  
And Control ◽  
Near Future

Managing the COVID-19 pandemic in the middle of the events requires real-time monitoring of its evolution to perform analyses of containment actions and to project near future scenarios. This work proposes a scheme to monitor the temporal evolution of the COVID-19 pandemic using the time series of its total number of confirmed cases in a given region. The monitored parameter is the spread rate obtained from this time series (day-1) expressed in %/day. The scheme's capability is verified using the epidemic data from China and South Korea. Its projection capability is shown for Italy and United States with scenarios for the ensuing 30 days from April 2nd, 2020. The spread rate (relative rate of change of the time series) is very sensitive to sudden changes in the epidemic evolution and can be used to monitor in real-time the effectiveness of containment actions. The logarithm of this variable allows identifying clear trends of the evolution of the COVID-10 epidemic in these countries. The spread rate calculated from the number of confirmed cases of infection is interpreted as a probability per unit of time of virus infection and containment actions. Its product with the number of confirmed cases of infections yields the number of new cases per day. The stabilization and control of the epidemic for China and South Korea appear to occur for values of this parameter below 0.77 %/day (doubling time of 90 days).

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 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.

Photonic Reservoir Computing for Real-Time Respiratory Motion Prediction

2021 ◽  
Author(s):  
Zhizhuo Liang ◽  
Meng Zhang ◽  
Chengyu Shi ◽  
Z. Rena Huang
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Absolute Error ◽  
Motion Prediction ◽  
Reservoir Computing ◽  
Data Set ◽  
Real Time Processing ◽  
The Real ◽  
Look Ahead ◽  
Clinical Radiotherapy

Abstract The application of reservoir computing (RC) is for the first time studied in a class of forecasting tasks in which signals are under random physical perturbations, meaning that the data-baring waveform distortions are versatile, and the process is not repeatable. Tumor movement caused by respiratory motion is such a problem and real-time prediction of tumor motion is required by the clinical radiotherapy. In this work, a true-time delay (TTD) respiration monitor based on photonic RC with adjustable nodes connection is developed specifically for this task. A breathing data set from a total of 76 patients with breathing speeds ranging from 3 to 20 breath per minute (BPM) are studied. A double-sliding window technology is demonstrated to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed tumor position data. Motion prediction of look-ahead times of 66.6 ms, 166.6 ms and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.0246, an average mean absolute error (MAE) of 0.338 mm, an average therapeutic beam exposure efficiency of 94.14% for an absolute error (AE) < 1mm and 99.89% for AE < 3mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.

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