1. Fuel gas operation management practices for reheating furnace in iron and steel industryD. M. Chen, 2020, original scientific article Abstract: How to evaluate the fuel gas operation (FGO) of various working groups (WGs) and working shifts (WSs) in reheating furnace is still ambiguous problem. In this paper, a novelty time-series FGO evaluation model was proposed. The strategy mainly included: Firstly, the fuel gas per ton steel (FGTS) was calculated in certain time interval; Secondly, the FGTS time-series data set was formulated in statistical period; Thirdly, the FGTS time-series data set was divided according to working schedule; Lastly, the FGO evaluation model was established. Case study showed that: i) The fuel gas operation evaluation results of various WGs in different WSs were accorded with normal distribution; ii) For various WGs, A WG performed best, followed by C WG and D WG. The performance of B WG was the worst due to its violent fluctuation of fuel gas operation evaluation results in three WSs; iii) For different WSs, the day WS and swing WS performed well, whereas the performance of night WS was unsatisfactory. Discussion results showed that the improvement of working skills, working responsibility and working passion, which were effective measure to achieve energy saving in terms of operation, should be enhanced through skills training and the reward and punishment system. Generally, this novelty time-series FGO evaluation method could also be applied to other industrial equipment.
Keywords: Iron industry, steel Industry, fuel gas operation management, FGO evaluation model, reheating furnace, fuel gas per ton steel time-series, working groups
Published in DKUM: 15.01.2026; Views: 0; Downloads: 0
 Full text (993,34 KB)
This document has many files! More...
This document is also a collection of 1 document! |
2. Temporal and statistical insights into multivariate time series forecasting of corn outlet moisture in industrial continuous-flow drying systemsMarko Simonič, Simon Klančnik, 2025, original scientific article Abstract: Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation.
Keywords: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Published in DKUM: 03.11.2025; Views: 0; Downloads: 5
Full text (3,02 MB)
This document has many files! More... |
3. Simulation-based study of structural changes in electrical time-series signalsLuka Živković, Željko Hederić, Tin Benšić, Goran Kurtović, Marinko Stojkov, 2025, original scientific article Abstract: his paper uses statistical indicators to address the detection of changes in electrical signals typical of industrial and power systems. A dedicated MATLAB algorithm was developed to identify change points by tracking shifts in signal behaviour and statistical properties. To evaluate the method, synthetic signals were generated through simulation to reproduce the common patterns observed in these systems, allowing testing under different operating conditions and varying noise levels. The results demonstrate that the algorithm detects change points reliably across multiple scenarios, showing flexibility and robustness. This study highlights the value of simulation-based signal generation as a controlled environment for testing detection methods. It provides a foundation for future applications to more complex real-world electrical signal analysis tasks.
Keywords: break points, energy system, noise, segmentation, signals, simulation, time series
Published in DKUM: 01.10.2025; Views: 0; Downloads: 2
 Link to file |
4. EEG-based finger movement classification with intrinsic time-scale decompositionMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2024, original scientific article Keywords: brain-computer interfaces, electroencephalogram, feature reduction, machine learning, finger movements classification, time series analysis
Published in DKUM: 16.04.2025; Views: 0; Downloads: 7
Full text (1,44 MB)
This document has many files! More... |
5. Financial system and agricultural growth in UkraineOlena Oliynyk, 2017, original scientific article Abstract: Background/Purpose: An effective financial system should increase the efficiency of economic activities. This study provides evidence regarding the importance of financial development for agricultural growth in Ukraine.
Methodology: We used non-integrated and integral indicators, time series and regression analysis to investigate the link between the financial development and agricultural growth.
Results: The results based on integral indicators shows that the financial development does not affect agricultural growth in Ukraine. The study based on non-integrated indicators, which characterizes various aspects of the financial system’s banking component and agricultural growth, provided a significant link between the financial system and agriculture growth. The regression models revealed if bank deposits to GDP (%) increases the value added per worker in agriculture increases exponentially. The results of the study indicate that, agriculture is more sensitive to lending changes than the vast majority of other sectors of the economy. The increasing lending of one UAH (Ukrainian hryvnia) resulted in retail turnover growth of 1.62 UAH, while agricultural gross output, growth was UAH 5.06.
Conclusion: Our results reveal a positive relationship between financial system’s banking component and agriculture growth in Ukraine. The results indicate the necessity for continued research into further developing universal methodological approaches of appraising the nexus of the financial system’s banking component on agriculture growth in general as well separate farm groups. The results of our study has important implications on policy making authorities efforts to stimulate agricultural growth by improving the efficiency of the financial system’s banking component.
Keywords: agricultural growth, the integral indicator of the agricultural growth, the integral indicator of the financial development, time series analysis, regression analysis, financial system
Published in DKUM: 04.05.2018; Views: 1477; Downloads: 419
Full text (762,63 KB)
This document has many files! More... |
6. Container throughput forecasting using dynamic factor analysis and ARIMAX modelMarko Intihar, Tomaž Kramberger, Dejan Dragan, 2017, original scientific article Abstract: The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.
Keywords: container throughput forecasting, ARIMAX model, dynamic factor analysis, exogenous macroeconomic indicators, time series analysis
Published in DKUM: 12.12.2017; Views: 2198; Downloads: 484
Full text (1,33 MB)
This document has many files! More... |
7. Forecasting the primary demand for a beer brand using time series analysisDanjel Bratina, Armand Faganel, 2008, original scientific article Abstract: Market research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.
Keywords: market research, time series forecasting, beer demand
Published in DKUM: 30.11.2017; Views: 1235; Downloads: 391
Full text (395,07 KB)
This document has many files! More... |
8. Introducing nonlinear time series analysis in undergraduate coursesMatjaž Perc, 2006, professional article Abstract: This article is written for undergraduate students and teachers who would like to get familiar with basic nonlinear time series analysis methods. We present a step-by-step study of a simple example and provide user-friendly programs that allow an easy reproduction of presented results. In particular, we study an artificial time series generated by the Lorenz system. The mutual information and false nearest neighbour method are explained in detail, and used to obtain the best possible attractor reconstruction. Subsequently, the times series is tested for stationarity and determinism, which are both important properties that assure correct interpretation of invariant quantities that can be extracted from the data set. Finally, as the most prominent invariant quantity that allows distinguishing between regular and chaotic behaviour, we calculate the maximal Lyapunov exponent. By following the above steps, we are able to convincingly determine that the Lorenz system is chaotic directly from the generated time series, without the need to use the differential equations. Throughout the paper, emphasis on clear-cut guidance and a hands-on approach is given in order to make the reproduction of presented results possible also for undergraduates, and thus encourage them to get familiar with the presented theory.
Keywords: nonlinear systems, nonlinear time series analyses, physics education
Published in DKUM: 07.06.2012; Views: 2089; Downloads: 39
Link to full text |
9. Singing of Neoconocephalus robustus as an example of deterministic chaos in insectsTina Perc Benko, Matjaž Perc, 2007, original scientific article Abstract: We use nonlinear time series analysis methods to analyse the dynamics of the sound-producing apparatus of the katydid Neoconocephalus robustus. We capture the dynamics by analysing a recording of the singing activity. First, we reconstruct the phase space from the sound recording and test it against determinism and stationarity. After confirming determinism and stationarity, we show that the maximal Lyapunov exponent of the series is positive, which is a strong indicator for the chaotic behaviour of the system. We discuss that methods of nonlinear time series analysis can yield instructive insights and foster the understanding of acoustic communication among insects.
Keywords: chaotic systems, chaos, time series, time series analyses, insect sounds, katydid
Published in DKUM: 07.06.2012; Views: 1923; Downloads: 530
Full text (1,05 MB)
This document has many files! More... |
10. Establishing the stochastic nature of intracellular calcium oscillations from experimental dataMatjaž Perc, Anne K. Green, C. Jane Dixon, Marko Marhl, 2008, original scientific article Abstract: Calcium has been established as a key messenger in both intra- and intercellular signaling. Experimentally observed intracellular calcium responses to different agonists show a variety of behaviors from simple spiking to complex oscillatory regimes. Here we study typical experimental traces of calcium oscillations in hepatocytes obtained in response to phenylephrine and ATP. The traces were analyzed with methods of nonlinear time series analysis in order to determine the stochastic/deterministic nature of the intracellular calcium oscillations. Despite the fact that the oscillations appear, visually, to be deterministic yet perturbed by noise, our analyses provide strong evidence that the measured calcium traces in hepatocytes are prevalently of stochastic nature. In particular, bursting calcium oscillations are temporally correlated Gaussian series distorted by a monotonic, instantaneous, time-independent function, whilst the spiking behavior appears to have a dynamical nonlinear component whereby the overall determinism level is still low. The biological importance of this finding is discussed in relation to the mechanisms incorporated in mathematical models as well as the role of stochasticity and determinism at cellular and tissue levels which resemble typical statistical and thermodynamic effects in physics.
Keywords: dynamic systems, stochastic processes, cellular signaling, calcium oscillations, time series analyses, noise, temporal correlation
Published in DKUM: 07.06.2012; Views: 1949; Downloads: 142
Link to full text |
