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Tytuł:
A hybrid approach to dimension reduction in classification
Autorzy:
Krawczak, M.
Szkatuła, G.
Powiązania:
https://bibliotekanauki.pl/articles/206425.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
data series
dimension reduction
envelopes
essential attributes
heteroassociation
machine learning from examples
decision rules
classification
Opis:
In this paper we introduce a hybrid approach to data series classification. The approach is based on the concept of aggregated upper and lower envelopes, and the principal components here called 'essential attributes', generated by multilayer neural networks. The essential attributes are represented by outputs of hidden layer neurons. Next, the real valued essential attributes are nominalized and symbolic data series representation is obtained. The symbolic representation is used to generate decision rules in the IF. . . THEN. . . form for data series classification. The approach reduces the dimension of data series. The efficiency of the approach was verified by considering numerical examples.
Źródło:
Control and Cybernetics; 2011, 40, 2; 527-551
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples
Autorzy:
Xinyun, Zhu
Sun, Jianzhong
Hu, Hanchun
Li, Chunhua
Powiązania:
https://bibliotekanauki.pl/articles/28086935.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
fault detection
semi-supervised
aircraft system
flight data
time-series data
Opis:
Health monitoring and fault detection of complex aircraft systems are paramount for ensuring reliable and efficient operation. The availability of monitoring data from modern aircraft onboard sensors provides a wealth of big data for developing deep learning-based fault detection methods. However, aircraft onboard systems typically have limited labeled fault samples and large amounts of unlabeled data. To better utilize the information contained in limited labeled fault samples, a deep learning-based semi-supervisedfault detection method is proposed, which leverages a small number of labeled fault samples to enhance its performance. A novel sample pairing strategy is introduced to improve algorithm performance by iteratively utilizing fault samples. A comprehensive loss function is employed to accurately reconstruct normal samples and effectively separate fault samples. The results of a case study using real data from a commercial aircraft fleet demonstrate the superiority of the proposed method over existing techniques, with improvements of approximately 16.7% in AP, 9.5% in AUC, and 19.2% in F1 score. Ablation studies confirm that performance can be further improved by incorporating additional labeled fault samples during training. Furthermore, the algorithm demonstrates good generalization ability.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 174382
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zmienność temperatury powietrza w Bydgoszczy w latach 1931–2013
Variability of air temperature in Bydgoszcz in the years 1931–2013
Autorzy:
Kasperska-Wołowicz, W.
Bolewski, T.
Powiązania:
https://bibliotekanauki.pl/articles/338222.pdf
Data publikacji:
2015
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
kwantylowa klasyfikacja termiczna
trend
wieloletnia seria danych
zmiany klimatu
climate change
long term data series
quintile-based thermal classification
Opis:
Celem pracy była charakterystyka warunków termicznych oraz analiza zmienności temperatury powietrza w Bydgoszczy w latach 1931–2013. Badaniami objęto temperaturę średnią roczną, półrocza letniego (IV–IX) i zimowego (X–III) oraz miesięczną. Średnia temperatura powietrza w analizowanym wieloleciu wyniosła 8,5°C. Zauważono istotny trend rosnący średniej rocznej temperatury powietrza w Bydgoszczy, który wyniósł 0,19°C na 10 lat. Silniejszy trend rosnący objął półrocze zimowe (0,23°C na 10 lat), zaś słabszy półrocze letnie (0,14°C na 10 lat). Półrocze zimowe charakteryzowało się również większą niż letnie zmiennością temperatury. Wyraźne ocieplenie półrocza zimowego obserwowano w latach 70. XX w., a półrocza letniego dekadę później. Określono średnią temperaturę roczną, półroczy i miesięcy o określonym prawdopodobieństwie nieprzewyższenia, na podstawie którego dokonano klasyfikacji termicznej badanych okresów. Wyszczególniono okresy, w których obserwowano ekstremalnie i anomalnie niską bądź wysoką temperaturę powietrza. Udział lat ekstremalnie i anomalnie chłodnych wyniósł łącznie 12,0%, zaś ekstremalnie i anomalnie ciepłych – 8,4%. Lata normalne stanowiły 16,9% w badanym okresie wieloletnim. Ze względu na długą (83 lata) serię danych o temperaturze powietrza w Bydgoszczy wyniki analizy mogą być wykorzystane do analiz porównawczych z innymi wieloletnimi ciągami pomiarów meteorologicznych. Mogą być również przydatne do analizy i oceny zmian klimatu oraz oceny warunków termicznych zarówno w minionych, jak i następnych latach.
The aim of the paper was to characterise thermal conditions and to analyse air temperature variability in Bydgoszcz in the years 1931–2013. The study included annual, summer half-year (April– September), winter half-year (October–March) and monthly mean temperatures. The average air temperature in the analysed long term period was 8.5°C. We noticed a significant increasing trend in the mean annual air temperature in Bydgoszcz, which raised by 0.19°C per 10 years. Stronger increasing trend was observed for winter half-year (0.23°C per 10 years) than for summer half-year (0.14°C per 10 years). Winter months were characterised by greater temperature variability than summer months. Distinct warming of the winter half-year was observed in the 1970s, that for summer half-year – one decade later. We determined mean annual, half-year and monthly temperature for a given probability of nonexceedance. On this basis we performed thermal classification of examined periods (year, half-year, month). We specified periods with extremely and abnormally low or high air temperatures. The share of the extremely and abnormally cold years was 12.0% whereas that of extremely and abnormally warm years – 8.4%. Normal years accounted for 16.9% in the whole 83-year study period. Due to the long data series of temperature in Bydgoszcz (83 years), the results of the analysis can be used for comparative analysis of other long lasting temperature series. They can also be useful for the analysis and assessment of climate change and assessment of thermal conditions either in the past or in the future.
Źródło:
Woda-Środowisko-Obszary Wiejskie; 2015, 15, 3; 25-43
1642-8145
Pojawia się w:
Woda-Środowisko-Obszary Wiejskie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Interpolation and Extrapolation of Newton and Cubic Splines to Estimate and Predict the Gas Content of Hydrogen and Iodine in the Formation of Iodic Acid Reactions
Autorzy:
Maryati, Ati
Pandiangan, Naomi
Purwani, Sri
Powiązania:
https://bibliotekanauki.pl/articles/1193309.pdf
Data publikacji:
2021
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Cubic Spline Interpolation
Extrapolation and Time series data
Newtom Interpolation
Opis:
The problem that is mostly related to the pattern of experimental time series data is the function that involves the data. Experimental data in the field of exact sciences is very important to conclude a problem. Existing data can form certain functions. In this research, we are looking for a function that represents the gas content of hydrogen and iodine in the reaction of acid iodide formation. This is achieved by using interpolation in which the function interpolates a given group of data points. Interpolation can also be used to evaluate the function at points different from the group. In addition to constructing and evaluating a functions by interpolation, we can also predict experimental data outside the given group of data points by using extrapolation. The results of data extrapolation can be used as an alternative to experimental data, thereby saving time and cost. This research will also compare interpolation and extrapolation of both Newton method and cubic splines, which one better interpolates and extrapolates data on hydrogen and iodine gas content in the reaction of acid iodide formation. The research results show that the cubic spline method is better than Newton method at approaching data, in terms of interpolation, as well as extrapolation.
Źródło:
World Scientific News; 2021, 153, 2; 124-141
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance measurement with high-performance computer using HW-GA anomaly-detection algorithms for streaming data
Autorzy:
Fondaj, Jakup
Hasani, Zirije
Krrabaj, Samedin
Powiązania:
https://bibliotekanauki.pl/articles/27312908.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
time-series data
HW-GA
anomaly detection
big streaming data
Numenta
COVID-19 data set
high-performance computer
Libelium sensor data
e-dnevnik
Opis:
Anomaly detection for streaming real-time data is very important; more significant is the performance of an algorithm in order to meet real-time requirements. Anomaly detection is very crucial in every sector because, by knowing what is going wrong with data/digital systems, we can make decisions to help in every sector. Dealing with real-time data requires speed; for this reason, the aim of this paper is to measure the performance of our proposed Holt–Winters genetic algorithm (HW-GA) as compared to other anomaly-detection algorithms with a large amount of data as well as to measure how other factors such as visualization and the performance of the testing environment affect the algorithm’s performance. The experiments will be done in R with different data sets such as the as real COVID-19 and IoT sensor data that we collected from Smart Agriculture Libelium sensors and e-dnevnik as well as three benchmarks from the Numenta data sets. The real data has no known anomalies, but the anomalies are known in the benchmark data; this was done in order to evaluate how the algorithm works in both situations. The novelty of this paper is that the performance will be tested on three different computers (in which one is a high-performance computer); also, a large amount of data will be used for our testing, as will how the visualization phase affects the algorithm’s performance.
Źródło:
Computer Science; 2022, 23 (3); 395--410
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Functional Regression in Short-Term Prediction of Economic Time Series
Autorzy:
Kosiorowski, Daniel
Powiązania:
https://bibliotekanauki.pl/articles/465836.pdf
Data publikacji:
2014
Wydawca:
Główny Urząd Statystyczny
Tematy:
functional data analysis
functional time series
prediction
Opis:
We compare four methods of forecasting functional time series including fully functional regression, functional autoregression FAR(1) model, Hyndman & Shang principal component scores forecasting using one-dimensional time series method, and moving functional median. Our comparison methods involve simulation studies as well as analysis of empirical dataset concerning the Internet users behaviours for two Internet services in 2013. Our studies reveal that Hyndman & Shao predicting method outperforms other methods in the case of stationary functional time series without outliers, and the moving functional median induced by Frainman & Muniz depth for functional data outperforms other methods in the case of smooth departures from stationarity of the time series as well as in the case of functional time series containing outliers.
Źródło:
Statistics in Transition new series; 2014, 15, 4; 611-626
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative analysis of methods for hourly electricity demand forecasting in the absence of data - a case study
Analiza porównawcza metod prognozowania godzinnego zapotrzebowania na energię elektryczną przy brakach w danych - studium przypadku
Autorzy:
Zawadzki, Jan
Powiązania:
https://bibliotekanauki.pl/articles/2194900.pdf
Data publikacji:
2023
Wydawca:
Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
Tematy:
forecasting
missing data
time series
high frequency
Opis:
Scope and purpose of work: This paper examines the impact of the number of gaps in data, the analytical form, and the model type selection criterion on the accuracy of interpolation and extrapolation forecasts for hourly data. Materials and methods: Forecasts were developed on the basis of predictors that are based on: classical time series forecasting models and regression time series forecasting models, hybrid time series forecasting models and hybrid regression forecasting models for uncleared series, and exponential smoothing models for cleared series of two or three types of seasonal fluctuations, with minimum estimates of errors in interpolation or extrapolation forecasts. Results: Adaptive and hybrid regression models have proved to have the most favorable predictive properties. Most hybrid time series models for systematic and non-systematic gaps and for both analytical forms are single models that generally describe fluctuations within a 24-hour cycle. Conclusions: The lowest estimators of prediction errors involving interpolation were obtained for exponential smoothing models, followed by hybrid regression models. A reverse sequence was obtained for extrapolative forecasting.
Źródło:
Economic and Regional Studies; 2023, 16, 1; 34-50
2083-3725
2451-182X
Pojawia się w:
Economic and Regional Studies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modele hierarchiczne w prognozowaniu zmiennych o wysokiej częstotliwości obserwowania w warunkach braku pełnej informacji
Hierarchical models in forecasting of the high-frequency variables in the conditions of lack of full information
Autorzy:
Szmuksta-Zawadzka, Maria
Zawadzki, Jan
Powiązania:
https://bibliotekanauki.pl/articles/425235.pdf
Data publikacji:
2014
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
high-frequency data
hierarchical models
incomplete time series
Opis:
The paper presents a procedure of application of regular hierarchical models in forecasting missing data in high-frequency time series with cyclical fluctuations. Annual, weekly and daily cycles of seasonal fluctuation have additive character. Separately regular hierarchical models have been built for even length cycles.Theoretical considerations are illustrated with an empirical example.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2014, 4(46); 72-84
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational intensive methods for prediction and imputation in time series analysis
Autorzy:
Neves, Maria
Cordeiro, Clara
Powiązania:
https://bibliotekanauki.pl/articles/729950.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
bootstrap
forecast intervals
missing data
time series analysis
Opis:
One of the main goals in times series analysis is to forecast future values. Many forecasting methods have been developed and the most successful are based on the concept of exponential smoothing, based on the principle of obtaining forecasts as weighted combinations of past observations. Classical procedures to obtain forecast intervals assume a known distribution for the error process, what is not true in many situations. A bootstrap methodology can be used to compute distribution free forecast intervals. First an adequately chosen model is fitted to the data series. Afterwards, and inspired on sieve bootstrap, an AR(p) is used to filter the series of the random component, under the stationarity hypothesis. The centered residuals are then resampled and the initial series is reconstructed. This methodology will be used to obtain forecasting intervals and for treating missing data, which often appear in a real time series. An automatic procedure was developed in R language and will be applied in simulation studies as well as in real examples.
Źródło:
Discussiones Mathematicae Probability and Statistics; 2011, 31, 1-2; 121-139
1509-9423
Pojawia się w:
Discussiones Mathematicae Probability and Statistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of the Savitzky-Golay method for power output data set
Autorzy:
Gulkowski, S.
Powiązania:
https://bibliotekanauki.pl/articles/1940714.pdf
Data publikacji:
2015
Wydawca:
Politechnika Gdańska
Tematy:
photovoltaics
solar energy
time series
PV data analysis
Opis:
The power output of a PV system changes in time during the day and strongly depends on the location and orientation of the photovoltaic module as well as on seasonal conditions. Clouds occurring during a partly cloudy day are the reason why this data is very irregular and difficult to analyze in terms of obtaining energy. The Savitzky-Golay method was applied for the power output data obtained for sunny, cloudy and partly cloudy days in order to determine the average level of power produced by a PV system at a given location. The total amount of energy was analyzed for each case.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2015, 19, 1; 25-34
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Energy associated tuning method for short-term series forecasting by complete and incomplete datasets
Autorzy:
Rodríguez-Rivero, C.
Pucheta, J.
Laboret, S.
Sauchelli, V.
Patińo, D.
Powiązania:
https://bibliotekanauki.pl/articles/91842.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
short time series
forecasting
missing data
energy associated to series
complete datasets
incomplete datasets
Opis:
This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 1; 5-16
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Transnational TV Series Adaptations: What Artificial Intelligence Can Tell Us About Gender Inequality In France And The US
Autorzy:
Digeon, Landry
Amin, Anjal
Powiązania:
https://bibliotekanauki.pl/articles/2150853.pdf
Data publikacji:
2021
Wydawca:
Univerzita sv. Cyrila a Metoda. Fakulta masmediálnej komunikácie
Tematy:
Transnational TV series
Artificial intelligence
Big data
Gender inequality
Opis:
The present research analyzes the inequality of gender representation in transnational TV series. For this purpose, a content analysis was carried out on 18 episodes of the US crime show Law & Order: Criminal Intent and its French adaptation Paris Enquêtes Criminelles. To conduct this research, we used the artificial intelligence toolkit the Möbius Trip, which is equipped with a gender and emotion recognition feature and relies on big data. The main findings indicate that male characters overwhelmingly dominate the onscreen time equally in both the US and the French versions. The data also show that male characters are more emotionally expressive and that women tend to display a wider range of emotions. The French characters are slightly more emotionally expressive than their American counterparts. The data also suggest that male characters tend to display violent behavior and that female characters tend to be portrayed as a victim in both versions of the show. The emotions-related results show a trend, but the difference of emotions between male and female characters and between the French and American cultures remain fairly narrow.
Źródło:
Media Literacy and Academic Research; 2021, 4, 1; 6-23
2585-8726
Pojawia się w:
Media Literacy and Academic Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Synchronization of data recorded using acquisition stations with data from camera during the bubble departure
Autorzy:
Dzienis, P.
Mosdorf, R.
Powiązania:
https://bibliotekanauki.pl/articles/102394.pdf
Data publikacji:
2013
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
experimental data synchronization
time series
video analysis
bubble departure
Opis:
In this study the first part of the experimental data was recorded in a data acquisition station, and another one was recorded with a high speed camera. The data recorded using the acquisition station was recorded with higher frequency than the time between two subsequent frames of the film. During the analysis of the experimental data the problem was related to the synchronization of measurement from acquisition station and data recorded with a camera. In this paper the method of synchronization of experimental data has been shown. A laser- phototransistor system has been used. The data synchronization was required in scaling of sampling frequency in the investigated time series.
Źródło:
Advances in Science and Technology. Research Journal; 2013, 7, 20; 29-34
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An integrated approach to estimate storage reliability with masked data from series system
Autorzy:
Zhang, Yongjin
Zhao, Ming
Powiązania:
https://bibliotekanauki.pl/articles/27322965.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
storage reliability
masked data
EM-LS algorithm
series system
LS method
Opis:
Storage reliability is of importance for the products that largely stay in storage in their total life-cycle such as warning systems for harmful radiation detection, and many kinds of defense systems, etc. Usually, the field-testing data can be available, but the failure causes for a series system cannot be always known because of the masked information. In this paper, the storage reliability model with possibly initial failures is studied on the statistical analysis method when the masked data are considered. To optimize the use of the masked survival data from storage systems, a technique based on the least squares (LS) method with an EM-like algorithm, is proposed for the series system. The parametric estimation procedure based on the LS method is developed by applying the algorithm to update the testing data, and then the LS estimation for the initial reliability and failure rate of the components constituting the series system are investigated. In the case of exponentially distributed storage lifetime, a numerical example is provided to illustrate the method and procedure. The results should be useful for accurately evaluating the production reliability, identifying the production quality, and planning a storage environment.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 172922
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting in multivariate incomplete time series. Application of the expectation-maximisation algorithm supplemented by the Newton-Raphson method
Autorzy:
Korczyński, Adam
Powiązania:
https://bibliotekanauki.pl/articles/1806793.pdf
Data publikacji:
2021-08-24
Wydawca:
Główny Urząd Statystyczny
Tematy:
missing data
multivariate time series
expectation-maximisation algorithm
Newton-Raphson algorithm
Opis:
Statistical practice requires various imperfections resulting from the nature of data to be addressed. Data containing different types of measurement errors and irregularities, such as missing observations, have to be modelled. The study presented in the paper concerns the application of the expectation-maximisation (EM) algorithm to calculate maximum likelihood estimates, using an autoregressive model as an example. The model allows describing a process observed only through measurements with certain level of precision and through more than one data series. The studied series are affected by a measurement error and interrupted in some time periods, which causes the information for parameters estimation and later for prediction to be less precise. The presented technique aims to compensate for missing data in time series. The missing data appear in the form of breaks in the source of the signal. The adjustment has been performed by the EM algorithm to a hybrid version, supplemented by the Newton-Raphson method. This technique allows the estimation of more complex models. The formulation of the substantive model of an autoregressive process affected by noise is outlined, as well as the adjustment introduced to overcome the issue of missing data. The extended version of the algorithm has been verified using sampled data from a model serving as an example for the examined process. The verification demonstrated that the joint EM and Newton-Raphson algorithms converged with a relatively small number of iterations and resulted in the restoration of the information lost due to missing data, providing more accurate predictions than the original algorithm. The study also features an example of the application of the supplemented algorithm to some empirical data (in the calculation of a forecasted demand for newspapers).
Źródło:
Przegląd Statystyczny; 2021, 68, 1; 17-46
0033-2372
Pojawia się w:
Przegląd Statystyczny
Dostawca treści:
Biblioteka Nauki
Artykuł
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