scholarly journals Devising a self-adjusting zero-order Brown’s model for predicting irreversible processes and phenomena

2021 ◽  
Vol 5 (10 (113)) ◽  
pp. 40-47
Author(s):  
Boris Pospelov ◽  
Vladimir Andronov ◽  
Evgenіy Rybka ◽  
Olekcii Krainiukov ◽  
Nadiya Maksymenko ◽  
...  
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Time Moment ◽  
Zero Order ◽  
Smoothing Parameter ◽  
The Self ◽  
Irreversible Processes ◽  
Limit Set ◽  
Experimental Time Series ◽  
Chaotic Nature

A self-adjusting zero-order Brown’s model has been devised. This model makes it possible to predict with high accuracy not only fires in the premises but also irreversible processes and phenomena of a random and chaotic nature under actual conditions. The essence of the self-adjusting model is that, based on Kalman’s approach, it is proposed to set the smoothing parameter for each time moment. Such a parameter is determined depending on the resulting current forecast error, taking into consideration the real and unknown dynamics of the studied series and noise. That does not require the selection of the smoothing parameter characteristic of known models. In addition, the proposed Brown’s model, unlike the known modifications, does not require setting a dynamics model of the level of the examined time series. The self-adjusting model provides negligible errors and efficiency of the forecast. The operability of the devised model was checked using an example of the experimental time series for the current measure of the recurrence of the increments of the state of the air medium in the laboratory chamber during alcohol combustion. As quantitative indicators of the quality of the forecast error, the current values for the square and absolute values were considered. It has been established that the current square of the forecast error is more than six orders of magnitude smaller compared to the case of a fixed smoothing parameter from a beyond-the-limit set. However, the current square of the forecast error for abrupt changes in the dynamics of the series level is half that of the fixed parameter of the beyond-the-limit set. It is noted that the results confirm the feasibility of the proposed self-adjusting Brown’s model

scholarly journals Short-term fire forecast based on air state gain recurrence and zero-order brown model

2021 ◽  
Vol 3 (10(111)) ◽  
pp. 27-33
Author(s):  
Boris Pospelov ◽  
Evgenіy Rybka ◽  
Ruslan Meleshchenko ◽  
Olekcii Krainiukov ◽  
Igor Biryukov ◽  
...  
Keyword(s):  
Indoor Air ◽  
Forecast Error ◽  
Sharp Decrease ◽  
Zero Order ◽  
Smoothing Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Limit Set ◽  
Short Term ◽  
Model Parameter

Possibilities of parameterization of the zero-order Brown model for indoor air forecasting based on the current measure of air state gain recurrence are considered. The key to the zero-order parametric Brown forecasting model is the selection of the smoothing parameter, which characterizes forecast adaptability to the current air state gain recurrence measure. It is shown that for effective short-term indoor fire forecast, the Brown model parameter must be selected from the out-of-limit set defined by 1 and 2. The out-of-limit set for the Brown model parameter is an area of effective fire forecasting based on the measure of current indoor air state gain recurrence. Errors of fire forecast based on the parameterized zero-order Brown model in the case of the classical and out-of-limit sets of the model parameters are investigated using the example of ignition of various materials in a laboratory chamber. As quantitative indicators of forecast quality, the absolute and mean forecast errors exponentially smoothed with a parameter of 0.4 are investigated. It was found that for alcohol, the smoothed absolute and mean forecast errors for the classical smoothing parameter in the no-ignition interval do not exceed 20 %. At the same time, for the out-of-limit case, the indicated forecast errors are, on average, an order of magnitude smaller. Similar ratios for forecast errors remain in paper, wood and textile ignition. However, for the transition zone corresponding to the time of material ignition, a sharp decrease in the current measure of chamber air state gain recurrence is observed. It was found that for this zone, the smoothed absolute forecast error for alcohol is about 2 % if the model parameter is selected from the classical set. If the model parameter is selected from the out-of-limit set, the forecast error is about 0.2 %. The results generally demonstrate significant advantages of using the zero-order Brown parametric model with out-of-limit model parameters for indoor fire forecasting

Characterising experimental time series using local intrinsic dimension

1995 ◽  
Vol 202 (2-3) ◽  
pp. 183-190 ◽  
Author(s):  
Thorsten M. Buzug ◽  
Jens von Stamm ◽  
Gerd Pfister
Keyword(s):  
Time Series ◽  
Intrinsic Dimension ◽  
Experimental Time ◽  
Experimental Time Series

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.  

scholarly journals Identification of Phase-Locked Loop System from Its Experimental Time Series

2021 ◽  
pp. 1-1
Author(s):  
Mikhail A. Mishchenko ◽  
Denis I. Bolshakov ◽  
Alexander S. Vasin ◽  
Valery V. Matrosov ◽  
Ilya V. Sysoev
Keyword(s):  
Time Series ◽  
Phase Locked Loop ◽  
Loop System ◽  
Experimental Time ◽  
Experimental Time Series

scholarly journals Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis

2013 ◽  
Vol 10 (80) ◽  
pp. 20120935 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Brian Ingalls ◽  
Eduardo Sontag
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Rhodobacter Sphaeroides ◽  
Time Series Data ◽  
Series Data ◽  
Transient Dynamic ◽  
Experimental Time ◽  
Theoretical Assumptions ◽  
Experimental Time Series ◽  
Fold Change Detection

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides shares many similarities with that of Escherichia coli . It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have correctly predicted that the E. coli output behaviour is unchanged under scaling of its ligand input signal; this property is known as fold-change detection (FCD). In the light of recent experimental results suggesting that R. sphaeroides may also show FCD, we present theoretical assumptions on the R. sphaeroides chemosensory dynamics that can be shown to yield FCD behaviour. Furthermore, it is shown that these assumptions make FCD a property of this system that is robust to structural and parametric variations in the chemotaxis pathway, in agreement with experimental results. We construct and examine models of the full chemotaxis pathway that satisfy these assumptions and reproduce experimental time-series data from earlier studies. We then propose experiments in which models satisfying our theoretical assumptions predict robust FCD behaviour where earlier models do not. In this way, we illustrate how transient dynamic phenotypes such as FCD can be used for the purposes of discriminating between models that reproduce the same experimental time-series data.

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