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1

Using deep neural networksto improve the precision of fast-sampled particle timing detectors

Kocot Mateusz, Misan Krzysztof, Avati Valentina, Bossini Edoardo, Grzanka Leszek, Minafra Nicola

Computer Science

|

2024

|

T. 25 (1)

43--61

EN

Measurements from particle timing detectors are often affected by the timewalk effect caused by statistical fluctuations in the charge deposited by passingparticles. The constant fraction discriminator (CFD) algorithm is frequentlyused to mitigate this effect both in test setups and in running experiments,such as the CMS-PPS system at the CERN’s LHC. The CFD is simple andeffective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonlyused for time series analysis, including computing the particle arrival time. Weevaluated various neural network architectures using data acquired at the testbeam facility in the DESY-II synchrotron, where a precise MCP (MicroChan-nel Plate) detector was installed in addition to PPS diamond timing detectors.MCP measurements were used as a reference to train the networks and com-pare the results with the standard CFD method. Ultimately, we improved thetiming precision by 8% to 23%, depending on the detector’s readout channel.The best results were obtained using a UNet-based model, which outperformedclassical convolutional networks and the multilayer perceptron.

2

Variations of temperature, salinity and oxygen of the Baltic Sea for the period 1950 to 2020

Stockmayer Vera, Lehmann Andreas

Oceanologia

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2023

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Vol. 65 (3)

466--483

EN

Variations of temperature, salinity and oxygen of the Baltic Sea on interannual to decadal timescales were studied for the period from 1950 to 2020. Both observational data and the output of a numerical circulation model of the Baltic Sea were analyzed. In addition, we investigated the influence of atmospheric parameters and river runoff on the observed hydrographic variations. Variability of sea surface temperature (SST) closely follows that of air temperature in the Baltic on all timescales examined. Interannual variations of SST are significantly correlated with the North Atlantic Oscillation in most parts of the sea in winter. The entire water column of the Baltic Sea has warmed over the period 1950 to 2020. The trend is strongest in the surface layer, which has warmed by 0.3–0.4°C decade−1, noticeably stronger since the mid-1980s. In the remaining water column, characterized by permanent salinity stratification in the Baltic Sea, warming trends are slightly weaker. A decadal variability is striking in surface salinity, which is highly correlated with river runoff into the Baltic Sea. Long-term trends over the period 1950–2020 show a noticeable freshening of the upper layer in the whole Baltic Sea and a significant salinity increase below the halocline in some regions. A decadal variability was also identified in the deep layer of the Baltic Sea. This can be associated with variations in saltwater import from the North Sea, which in turn are influenced by river runoff: fewer strong saltwater inflows were observed in periods of enhanced river runoff. Furthermore, our results suggest that changes in wind speed have an impact on water exchange with the North Sea. Interannual variations of surface oxygen are strongly anti-correlated with those of SST. Likewise, the positive SST trends are accompanied by a decrease in surface oxygen. In greater depths of the Baltic Sea, oxygen decrease is stronger, which is partly related to the observed increase of the vertical salinity gradient.

3

Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders

Pradeep Chakka Sai, Sinha Neelam

Biocybernetics and Biomedical Engineering

|

2023

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Vol. 43, no. 4

700--724

EN

Automated surgical video analysis promises improved healthcare. We propose novel spatial context aware combined loss function for end-to-end Encoder-Decoder training for Surgical Phase Classification (SPC) on laparoscopic cholecystectomy (LC) videos. Proposed loss function leverages on fine-grained class activation maps obtained from fused multi-layer Layer-CAM for supervised learning of SPC, obtaining improved Layer-CAM explanations. Post classification, we introduce graph theory to incorporate known hierarchies of surgical phases. We report peak SPC accuracy of 96.16%, precision of 94.08% and recall of 90.02% on public dataset Cholec80, with 7 phases. Our proposed method utilizes just 73.5% of parameters as against existing state-of-the-art methodology, achieving improvement of 0.5% in accuracy, 1.76% in precision with comparable recall, with an order less standard deviation. We also propose DNN based surgical skill assessment methodology. This approach utilizes surgical phase prediction scores from the final fully-connected layer of spatial-context aware classifier to form multi-channel temporal signal of surgical phases. Time-invariant representation is obtained from this temporal signal through time- and frequency-domain analyses. Autoencoder based time-invariant features are utilized for reconstruction and identification of prominent peaks in dissimilarity curves. We devise a surgical skill measure (SSM) based on spatial-context aware temporal-prominence-of-peaks curve. SSM values are expected to be high when executed skillfully, aligning with expert assessed GOALS metric. We illustrate this trend on Cholec80 and m2cai16-tool datasets, in comparison with GOALS metric. Concurrence in the trend of SSM with respect to GOALS metric is obtained on these test videos, making it a promising step towards automated surgical skill assessment.

4

Research on Earth rotation and geodynamics in Poland in 2019–2022

Bogusz Janusz, Brzezinski Aleksander, Godah Walyeldeen, Nastula Jolanta

Advances in Geodesy and Geoinformation

|

2023

|

Vol. 72, no. 2

art. no. e41, 2023

EN

This paper summarizes the activity of the chosen Polish geodetic research teams in 2019–2022 in the fields of the Earth rotation and geodynamics. This publication has been prepared for the needs of the presentation of Polish scientists’ activities on the 28th International Union of Geodesy and Geodynamics General Assembly, Berlin, Germany. The part concerning Earth rotation is mostly focused on the estimation of the geophysical excitation of polar motion using data from Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions, and on the improvement of the determination of Earth rotation parameters based on the Satellite Laser Ranging (SLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and Global Navigation Satellite System (GNSS) satellite techniques. The part concerning geodynamics is focused on geodetic time series analysis for geodynamical purposes and monitoring of the vertical ground movements induced by mass transport within the Earth’s system, monitoring of the crustal movements using GNSS and newly applied Interferometric Synthetic Aperture Radar (InSAR), discussing the changes of the landslides and its monitoring using geodetic methods as well as investigations of seismic events and sea-level changes with geodetic methods. Finally, the recent research activities carried out by Polish scientists in the international projects is presented.

5

Microseismic signal denoising using simple bandpass filtering based on normal time–frequency transform

Yao Yanji, Wang Guocheng, Liu Lintao

Acta Geophysica

|

2023

|

Vol. 71, no. 5

2217--2232

EN

In active underground mining environments, monitoring mine vibrations has important implications for both safety and productivity. Microseismic data processing is crucial for subsurface real-time monitoring during mineral mining processes. Microseismic events are difficult to detect due to their small magnitudes and low signal-to-noise ratios (SNRs). Useful microseismic signals are usually obscured by long-period microseisms, random noise and artificial strong noise. We propose a useful microseismic denoising algorithm based on the normal time–frequency transform (NTFT) to determine the instantaneous frequency, amplitude and phase information from useful microseismic signals. The energy difference in the time–frequency domain between useful microseismic signals and strong noise is small. Therefore, based on the different phase characteristics of microseismic signals and noise in the NTFT phase spectrum, noise can be filtered out by reconstructing the microseismic signals in useful real-time frequency bands. The proposed simple bandpass filtering (SBPF) method is advantageous because the denoising result does not produce phase shifts, energy leakage or artefacts. The only parameter of the proposed method that needs to be defined is the instantaneous cutoff frequency; thus, the denoising operation is simple. We use both synthetic and real data to demonstrate the feasibility of the method for denoising complicated microseismic datasets.

6

Investigation of signals of the range 10-3 - 10-4 Hz registered by water-tube tiltmeters in the underground geodynamic laboratory in Książ (SW Poland)

Kaczorowski Marek, Kasza Damian, Zdunek Ryszard, Wronowski Roman

Artificial Satellites : Journal of Planetary Geodesy

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2022

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Vol. 57, No. 4

210--236

EN

The Geodynamic Laboratory in Książ includes investigations of various kinds of geodynamic signals. Among others, we registered harmonic signals of the range 10-3 - 10-4 Hz. These signals had been found in the measurement series of the long water-tube (WT) tiltmeters. The discovered signals consist of two classes of harmonics associated with various kinds of phenomena. The first class of these signals belongs to viscoelastic vibrations of the Earth’s solid body, while the second class is produced possibly by the extremely long atmospheric infrasound waves. The signals of the vibrations of the Earth had been well recognized by the characteristic frequencies of the Earth’s free vibrations’ resonance, which occur mainly after strong earthquakes. The atmospheric pressure microvibrations affected the water level in the hydrodynamic systems of the WTs as a result of an inverse barometric effect. We observed that signals from both classes blend in the harmonics of similar frequencies and jointly affect the hydrodynamic systems of the WTs. We found that the amplitude of the secondclass signals strongly depends on the location of water-tube gauges inside the underground, while the amplitudes of the first-class signals are similar for all the gauges. These observations clearly indicate the atmospheric origin of the second class of registered signals.

7

The efficiency of single base and network RTK for Structural Health Monitoring

Topal Güldane Oku, Akpinar Burak

Advances in Geodesy and Geoinformation

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2022

|

Vol. 71, no. 2

art. no. e28, 2022

EN

With the developing technology and increasing construction, the importance of structural observations, which are of great significance in disaster management, has increased. Geodetic methods have been preferred in recent years due to their high accuracy and ease of use in Structural Health Monitoring (SHM) Surveys. In this study, harmonic oscillation tests have been carried out on a shake table to determine the usability of the Single Base and the Network Real-Time Kinematic (RTK) Global Navigation Satellite Systems (GNSS) method in SHM studies. It is aimed to determine the harmonic movements of different amplitudes and frequencies created by the shake table with 20 Hz multi-GNSS equipment. The amplitude and frequency values of the movements created using Fast Fourier Transform (FFT) and Time Series Analysis have been calculated. The precision of the analysis results has been determined by comparing the LVDT (Linear Variable Differential Transformer) data, which is the position sensor of the shake table, with the GNSS data. The advantages of the two RTK methods over each other have been determined using the calculated amplitude and frequency differences. As a result of all experiments, it has been determined that network and single base RTK GNSS methods effectively monitor structural behaviours and natural frequencies.

8

The study of seismicity of Alborz (Northern Iran) and Zagros (Southern Iran) regions by using time series analysis

Rahimi Mohammad, Zamani Ahmad, Ghotbi Ali Reza

Acta Geophysica

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2022

|

Vol. 70, no 1

27--37

EN

Since many years ago Iran faced multiple and sometimes destructive earthquakes because of its positions in seismic Alpidebelt, the regions of Alborz (in north of Iran) and Zagros (in south of Iran) are seismically important due to their dense population and the presence of economic and strategic organization in them, these zones with their high tectonic movements and their positions in seismic Alpide-belt have high potentials for large earthquakes, and 80% of Iran’s earthquakes happen in these zones. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values, in this study a proper model has been proposed for the earthquakes that occurred in two regions of Alborz and Zagros during 1900−2021, in all these regions time series are analyzed individually, and proper models are proposed for the prediction of empirical recurrence rate of the earthquake with magnitudes larger than 6.0 mb. Then, a compression was made between the proposed models and the earthquakes that occurred to determine their accuracy.

9

Wielomodelowe prognozowanie liczby pasażerów przewożonych krajowym transportem lotniczym w Stanach Zjednoczonych w 2021 r.

Kozicki Bartosz, Sowa Bogdan

Gospodarka Materiałowa i Logistyka

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2021

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nr 7

23--32

PL

W artykule przedstawiono dane dotyczące liczby pasażerów przewiezionych pasażerskim transportem lotniczym krajowym i międzynarodowym w Stanach Zjednoczonych w ujęciu miesięcznym w latach 2003–2020 i ich prognozowania na 2021 r. Badania rozpoczęto od analizy i oceny dwóch szeregów czasowych dotyczących liczby pasażerów przewiezionych transportem lotniczym pasażerskim w Stanach Zjednoczonych w ujęciu krajowym i międzynarodowym. Zbudowano model Kleina, za pomocą którego wykonano prognozowanie szeregu czasowego liczby pasażerów przewożonych transportem lotniczym krajowym w ujęciu miesięcznym na rok 2021. Zbudowany model jest połączeniem prognozowania ilościowego i jakościowego .

EN

The article presents data on the number of passengers transported by domestic and international passenger air transport in the United States on a monthly basis in the years 2003–2020 and their forecasting for 2021. The research began with the analysis and evaluation of two time series concerning the number of passengers transported by passenger air transport in the United States in terms of national and international approach. The Klein model was built, which was used to forecast the time series of the number of passengers transported by domestic air transport on a monthly basis for the year 2021. The constructed model is a combination of quantitative and qualitative forecasting.

10

A statistical study of COVID-19 pandemic in Egypt

Radwan Taha

Demonstratio Mathematica

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2021

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Vol. 54, nr 1

233--244

EN

The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA)model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

11

Evaluation of the displacements of the permanent GPS stations of Beni Haroun Dam geodetic network (Algeria)

Khelifa Sofane, Dekkiche Hicham, Kahlouche Salem, Boudina Samia

Acta Geophysica

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2021

|

Vol. 69, no. 6

2129--2143

EN

The purpose of this paper is the analysis of the daily coordinate time series of the five permanent GPS (Global Positioning System) stations of the geodetic monitoring network of the Beni-Haroun Dam (Algeria), in order to assess the spectral content of the dam displacements. The coordinate time series analysis was based on the singular spectrum analysis to assess their principal components (trend, seasonal components and noise in phase space), the spectral analysis to identify their noise spectrum (white or colored) and the wavelet thresholding method to determine their noise in frequency space. The results showed that the primary signal present in the analyzed time series is mainly composed of a trend and an annual component. The trend and the annual signal explain more than 95% of the total signal in the three coordinates (x, y, z) for all studied stations. The analyzed time series in the three coordinates (x, y, z) are characterized by a linear drift less than 1 mm/year, their annual amplitudes are in the range of 0.5–2 mm, and the amplitudes of their semiannual, four-monthly and quarterly signals are in the range of 0–0.5 mm. The noise spectrum in the analyzed time series is flicker noise, and the noise level is in the range of 0.2–0.7 mm, 0.3–0.5 mm and 0.5–1.2 mm in, respectively, x-, y- and z-coordinates. The low values of trend and noise level in the analyzed station coordinates indicate that the Beni-Haroun Dam is qualified as stable.

12

Effect of features extraction on improving LSTM network quality in ECG signal classification

Kłosowski Grzegorz

Przegląd Elektrotechniczny

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2020

|

R. 96, nr 12

194--197

EN

This article focuses on the extraction of features extracted from ECG measurement signals to improve the quality of LSTM network operation. Two features were distinguished from each individual sequence of ECG signals: instantaneous frequency (IF) and spectral entropy (SE). Both of these features are extracted from ECG signals using short-time Fourier transform. The applied approach enables the conversion of original measurement sequences into spectral images, from which IF and SE coefficients are then generated. As a result of the research, it was found that feature extraction significantly improves ECG signal classification both in terms of forecasting accuracy and in terms of network learning speed.

PL

W niniejszym artykule skupiono się na ekstrakcji cech wyodrębnionych z sygnałów pomiarowych EKG w celu poprawy jakości działania sieci LSTM. Z każdej indywidualnej sekwencji sygnałów EKG wyróżniono dwie cechy: częstotliwość chwilową (IF) i entropię widmową (SE). Obie te cechy są wyodrębniane z sygnałów EKG przy użyciu krótkotrwałej transformaty Fouriera. Zastosowane podejście umożliwia konwersję oryginalnych sekwencji pomiarowych na obrazy widmowe, z których następnie generowane są współczynniki IF i SE. W wyniku badań stwierdzono, że ekstrakcja cech znacząco poprawia klasyfikację sygnału EKG zarówno pod względem dokładności prognozowania, jak i szybkości uczenia się.

13

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

Acker Alexander, Wittkopp Thorsten, Nedelkoski Sasho, Bogatinovski Jasmin, Kao Odej

Annals of Computer Science and Information Systems

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2020

|

Vol. 21

7--10

EN

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

14

The application of the arima model in forecasting the passenger traffic on the example of border crossings between the subcarpathian province and Ukraine

Dejniak D.

Information Systems in Management

|

2018

|

Vol. 7, No. 3

155--170

EN

Accession of Poland to the European Union meant that its eastern border became the external frontier of the Community. The next step in the European integration was joining the Schengen Zone by Poland. Polish citizens may freely travel throughout the Schengen Zone and the state was obliged to tighten its eastern border. Under these circumstances conducting research on passenger traffic has become a vital issue, with particular focus on the eastern frontier. In the article an attempt is made at examining the possibility of forecasting passenger traffic on the example of border crossing points between the Subcarpathian Province and Ukraine using the ARIMA models. Confirmation of these possibilities seems to be crucial as the number of people crossing the border is characterized by high variability and sensitivity to the political situation. The study is based on the information provided by the Polish Border Guard. The conducted time series analysis is of a multi-purpose character. It may be used to support decision making processes of investment, organizational, as well as socio-political nature.

15

Real time search algorithm for observation outliers during monitoring engineering constructions

Latos D., Kolanowski B., Pachelski W., Sołoducha R.

Reports on Geodesy and Geoinformatics

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2017

|

Vol. 104

103--114

EN

Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).

16

Changes in seasonal streamflow extremes experienced in rivers of Northwestern South America (Colombia)

Pierini J.O., Restrepo J.C., Aguirre J., Bustamante A.M., Velásquez G.J.

Acta Geophysica

|

2017

|

Vol. 65, no. 2

377--394

EN

A measure of the variability in seasonal extreme streamflow was estimated for the Colombian Caribbean coast, using monthly time series of freshwater discharge from ten watersheds. The aim was to detect modifications in the streamflow monthly distribution, seasonal trends, variance and extreme monthly values. A 20-year length time moving window, with 1-year successive shiftments, was applied to the monthly series to analyze the seasonal variability of streamflow. The seasonal-windowed data were statistically fitted through the Gamma distribution function. Scale and shape parameters were computed using the Maximum Likelihood Estimation (MLE) and the bootstrap method for 1000 resample. A trend analysis was performed for each windowed-serie, allowing to detect the window of maximum absolute values for trends. Significant temporal shifts in seasonal streamflow distribution and quantiles (QT), were obtained for different frequencies. Wet and dry extremes periods increased significantly in the last decades. Such increase did not occur simultaneously through the region. Some locations exhibited continuous increases only at minimum QT.

17

Techniki budowy nieliniowych modeli diagnostycznych z wykorzystaniem szeregów czasowych drgań mechanicznych

Puchalski A.

Przegląd Mechaniczny

|

2016

|

nr 10

33--36

PL

W artykule omówiono zastosowanie metod analizy nieliniowych szeregów czasowych drgań do oceny ich wartości informacyjnej do celów diagnostycznych układów mechanicznych na przykładzie zespołów napędowych w pojazdach. Trzy techniki detekcji oraz ekstrakcji sygnałów obejmowały analizę w zrekonstruowanej, zanurzonej przestrzeni fazowej szeregu czasowego, prowadzącą do segmentacji sygnału w zakresie dynamiki chaotycznej i losowej, analizę multifraktalną oraz analizę rozkładów stabilnych prawdopodobieństwa.

EN

The paper discusses the use of methods of nonlinear time series analysis of vibrations to assess their informational value for the diagnostic purposes of the mechanical systems on the example of vehicle powertrain. Three detection and extraction techniques of signals included an analysis of the reconstructed embedded phase space of time series which leads to the segmentation of the signal in terms of the chaotic and random dynamics, multifractal analysis and the analysis of alpha-stable probability distributions.

18

Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process

Rodziewicz A., Perzyk M.

Archives of Foundry Engineering

|

2016

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Vol. 16, iss. 4

125--130

EN

The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

19

Wykorzystanie modelu ARIMA do analizy szeregu czasowego

Malska W., Wachta H.

Zeszyty Naukowe Politechniki Rzeszowskiej. Elektrotechnika

|

2015

|

z. 34 [292], nr 3

23--30

PL

W artykule zaprezentowano zastosowanie metody ARIMA służącej do analizy szeregu czasowego z trendem i sezonowością. Szereg czasowy jest jednym z rodzajów szeregów statystycznych, który można zdefiniować jako ciąg obserwacji pewnego zjawiska w kolejnych jednostkach czasu (latach, kwartałach, miesiącach, itp.). Analiza szeregów czasowych opiera się na głównym założeniu, że kolejne wartości rozważanej cechy (zmiennej) reprezentują kolejne pomiary wykonane w takiej samej jednostce czasu (w równych odstępach czasu). Zmienną niezależną jest czas (jednostka czasu). Obserwując różne zjawiska (w tym także związane z gospodarką elektroenergetyczną) często chcemy wiedzieć czy i jak zmieniają się w czasie, czyli jaka jest ich dynamika. Analiza szeregów czasowych stosowana jest głównie do podejmowania decyzji związanych z przyszłością. Rozważane zjawisko może podlegać pewnym prawidłowościom, których wykrycie i opis jest głównym celem analizy szeregów czasowych. W wielu przypadkach modele szeregów czasowych wykorzystywane są w celu wnioskowania o przyszłości badanego zjawiska (do prognozowania). Prognozowanie w ujęciu statystycznym to wnioskowanie o przyszłych wartościach szeregu czasowego, które oparte jest na danych czasowych lub analizie wartości, jakie przyjmują rozważane cechy statystyczne (zmienne). Przy analizie w dziedzinie czasu w szeregu czasowym można wyodrębnić pewne składowe (stały przeciętny poziom zjawiska, trend, cykle długookresowe, wahania sezonowe, wahania krótkookresowe, interwencje, składnik losowy (zakłócenie losowe)), przy czym nie wszystkie one muszą występować w konkretnym analizowanym szeregu. Metoda prognozowania zależy od składowych szeregu czasowego. Wyniki obliczeń z wykorzystaniem modelu ARIMA zaprezentowano korzystając z pakietu STATISTICA v. 10.0.

EN

The paper presents the application of the method used for the analysis of ARIMA time series with trend and seasonality. Time series is one of the types of statistical series, which can be defined as a series of observations of a phenomenon in the following units of time (years, quarters, months, etc.). Time series analysis based on the main idea that a further consideration of the characteristics (variable) represent the more measurements made in the same unit of time (at regular intervals). The independent variable is the time (unit of time). Observing different phenomena (including related to the economy electricity) often want to know whether and how they are changing over time, that is what is their dynamics. Time series analysis is mainly used to make decisions about the future. Considered phenomenon may be subject to certain regularities, which detect and description is the main objective of the analysis of time series. In many cases, time series models are used to apply for the future of the studied phenomenon (to predict). Forecasting is statistically inference about future values of the time series, which is based on the analysis of data or time values which take under consideration the statistical characteristics (variables). At the time domain analysis in time series can extract some components (constant average level of the phenomenon, a trend long-term cycles, seasonal fluctuations, fluctuations in short-term, interventions, random component (random disturbance)), and not all of them must be analyzed in a specific number of. Forecasting method depends on the components of the time series. The results of calculations using the ARIMA model is presented using STATISTICA v. 10.0.

20

Krótkoterminowe prognozowanie wielkości popytu z wykorzystaniem klasycznych metod predykcji na przykładzie międzynarodowego przedsiębiorstwa produkcyjno-dystrybucyjnego

Jurczyk K., Gdowska K.

Logistyka

|

2015

|

nr 4

9076--9084, CD3

PL

W pracy zaprezentowano wyniki badań polegających na doborze odpowiedniej metody do prognozowania wielkości popytu w międzynarodowym przedsiębiorstwie produkcyjno-dystrybucyjnym. Wykorzystane metody – naiwną, średniej ruchomej, wygładzania wykładniczego oraz wskaźników sezonowości – porównano ze sobą oraz zwrócono wagę co do zasadności ich stosowania. Obiektem badań było międzynarodowe przedsiębiorstwo produkcyjno-dystrybucyjne, którego nazwa została zakodowana jako Przedsiębiorstwo X. W badaniach przeanalizowano wielkość sprzedaży ośmiu wybranych produktów na rynku skandynawskim. Historyczne miesięczne dane sprzedaży pochodziły z lat 2005–2011.

EN

In the paper a short-term demand forecasting models for international production and distribution enterprise were compared. The object of analysis was an international production and distribution enterprise which brand name was coded as the “X Enterprise”. In the research eight chosen products’ sales volumes were analysed. Historical input data came from 2005–2011.