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Tytuł:
Applying Python’s Time Series Forecasting Method in Microsoft Excel – Integration as a Business Process Supporting Tool for Small Enterprises
Autorzy:
Litwin, Jolanta
Olech, Marcin
Szymusik, Anna
Powiązania:
https://bibliotekanauki.pl/articles/2069739.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
time series forecasting
python integration
excel integration
Opis:
The paper describes the current state of research, where integration of Microsoft Excel and Python interpreter, gives the business user the right tool to solve chosen business process analysis problems like: forecasting, classification or clustering. The integration is done by using Visual Basic for Application (VBA), as well as XLWings Python’s library. Both mechanisms serve as an interfaces between MS Excel and Python to allow the data exchange between each other. Creating the suitable Graphical User Interface (GUI) in Microsoft Excel, gives the business user opportunity to select specific data analysis method available in Python’s environment and set its parameters, without Python’s programming. Running the method by Python’s interpreter can bring the results, which are hard or even impossible to obtain by using Microsoft Excel only. However, the data analysis methods stored in the Python’s script, which are available to the business user, as well as VBA source code, must be designed and implemented by the data scientist. Sample, basic integration between Microsoft Excel and Python’s interpreter is presented in the paper. To present value-added of the proposed software solution, simple case study according to time series forecasting problem is described, where forecasting errors of different methods available in the Microsoft Excel and Python are presented and discussed. The paper ends with conclusions according to the results of the current researches and suggested directions of further research.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2021, 24(1); 115--133
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cuban consumer price index forecasting through transformer with attention
Autorzy:
Rosado, Reynaldo
Toledano-López, Orlando G.
González, Hector R.
Abreu, Aldis J.
Hernandez, Yanio
Powiązania:
https://bibliotekanauki.pl/articles/27314241.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
consumer price index
time series forecasting
transformer with attention
ARIMA
LSTM
Opis:
Recently, time series forecasting modelling in the Con‐ sumer Price Index (CPI) has attracted the attention of the scientific community. Several research projects have tackled the problem of CPI prediction for their countries using statistical learning, machine learning and deep neural networks. The most popular approach to CPI in several countries is the Autoregressive Integrated Mov‐ ing Average (ARIMA) due to the nature of the data. This paper addresses the Cuban CPI forecasting problem using Transformer with attention model over univariate dataset. The fine tuning of the lag parameter shows that Cuban CPI has better performance with small lag and that the best result was in = 1. Finally, the comparative results between ARIMA and our proposal show that the Transformer with attention has a very high performance despite having a small data set.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 2; 12--17
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving prediction models applied in systems monitoring natural hazards and machinery
Autorzy:
Sikora, M.
Sikora, B.
Powiązania:
https://bibliotekanauki.pl/articles/331302.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
zagrożenie naturalne
szereg czasowy
k-najbliższy sąsiad
natural hazards monitoring
regression rules
time series forecasting
k-nearest neighbors
Opis:
A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 477-491
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wpływ liczby „najbliższych sąsiadów” na dokładność prognoz ekonomicznych szeregów czasowych
Effect of the number of “nearest neighbors” on the accuracy of economic time series forecasts
Autorzy:
Miśkiewicz-Nawrocka, Monika
Powiązania:
https://bibliotekanauki.pl/articles/591526.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Metoda najbliższych sąsiadów
Prognozowanie szeregów czasowych
Rekonstrukcja przestrzeni stanów
State space reconstruction
The nearest neighbors method
Time series forecasting
Opis:
Metoda najbliższych sąsiadów jest jedną z metod prognozowania szeregów czasowych. W metodzie tej, prognozę (N+1)-go elementu ˆN+1 x szacuje się jako średnią ważoną obserwacji xi+1, gdzie wektory d i x są k najbliższymi sąsiadami wektora d N x w zrekonstruowanej d-wymiarowej przestrzeni stanów. Istotnym problemem podczas stosowania tej metody jest wyznaczenie prawidłowej liczby najbliższych sąsiadów, która powinna być brana pod uwagę przy wyznaczaniu prognoz. Głównym celem artykułu jest zbadanie wpływu liczby najbliższych sąsiadów na dokładność prognoz ekonomicznych szeregów czasowych. Badania zostały przeprowadzone w oparciu o wybrane finansowe szeregi czasowe.
One of time series forecasting method is the nearest neighbors method. In this method, the forecast for (N+1)-th element ˆN +1 x is estimated as a weighted average of observations i+1 x , where the vectors d i x are k nearest neighbors of vector d N x in the reconstructed d-dimensional state space. An important problem when using nearest neighbors method is to determine the correct number of nearest neighbors, that should be taken into account in the determination of forecasts. The aim of the article will be to research the effect of the number of nearest neighbors on the accuracy of economic time series forecasts. The test will be conducted on the basis of selected financial time series.
Źródło:
Studia Ekonomiczne; 2016, 295; 60-69
2083-8611
Pojawia się w:
Studia Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wspomaganie planowania wielkości zapotrzebowania na klej poliuretanowy w kopalni węgla kamiennego
Demand planning support for polyurethane adhesive in coal mine
Autorzy:
Jakowska-Suwalska, K.
Sojda, A.
Wolny, M.
Powiązania:
https://bibliotekanauki.pl/articles/322466.pdf
Data publikacji:
2012
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
kopalnia węgla kamiennego
zarządzanie materiałami
prognoza szeregów czasowych
model ekonometryczny
poliuretan
hard coal mine
materials management
time series forecasting
econometric model
polyurethane
Opis:
Praca przedstawia propozycję metody wspomagania planowania zapotrzebowania na klej poliuretanowy, która bazuje na metodach prognozowania szeregów czasowych oraz na podstawie modelu ekonometrycznego. Jako finalny model prognostyczny wspomagający planowanie wielkości zapotrzebowania zaproponowano kombinowany model agregujący prognozy postawione za pomocą wybranych modeli. Agregacja polega na zastosowaniu sumy ważonej, przy tym wagi ustalono na podstawie kryterium minimalnego błędu prognoz wygasłych.
In this paper proposal of method for polyurethane adhesive demand planning support is presented. The method is based on models of time series forecasting and econometric model. The proposal is to combine the forecasts through application of weighted sum. The weight factors are determined by the minimal mean error of extinct forecasts criterion.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2012, 61; 127-138
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Influence of IQT on research in ICT
Autorzy:
Bednarski, Bogdan J.
Lepak, Łukasz E.
Łyskawa, Jakub J.
Pieńczuk, Paweł
Rosoł, Maciej
Romaniuk, Ryszard S.
Powiązania:
https://bibliotekanauki.pl/articles/2055259.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ICT
control theory
IQT
Information Quantum Technologies
Quantum 2.0
applications of IQT
quantum systems
qubit neural networks
quantum time series forecasting;
Quantum Reinforcement Learning
Opis:
This paper is written by a group of Ph.D. students pursuing their work in different areas of ICT, outside the direct area of Information Quantum Technologies IQT. An ambitious task was undertaken to research, by each co-author, a potential practical influence of the current IQT development on their current work. The research of co-authors span the following areas of ICT: CMOS for IQT, QEC, quantum time series forecasting, IQT in biomedicine. The intention of the authors is to show how quickly the quantum techniques can penetrate in the nearest future other, i.e. their own, areas of ICT.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 259--266
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Autorzy:
Li, C.
Chiang, T. W.
Powiązania:
https://bibliotekanauki.pl/articles/331280.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
zbiór rozmyty
system neuronowo-rozmyty
optymalizacja rojem cząstek
szereg czasowy
complex fuzzy set
complex neuro fuzzy system
hierarchical multi swarm
particle swarm optimization (PSO)
recursive least squares estimator
time series forecasting
Opis:
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deterministic chaos and forecasting in Amazon?s share prices
Autorzy:
Hanias, Michael
Tsakonas, Stefanos
Magafas, Lykourgos
Thalassinos, Eleftherios I.
Zachilas, Loukas
Powiązania:
https://bibliotekanauki.pl/articles/22444422.pdf
Data publikacji:
2020
Wydawca:
Instytut Badań Gospodarczych
Tematy:
time series
chaos theory
econophysics
forecasting
Opis:
Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics. Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100. Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price. Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2020, 15, 2; 253-273
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
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:
Analysis and Modeling of Domain Registration Process
Autorzy:
Arabas, P.
Jaskóła, P.
Kamola, M.
Karpowicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/309217.pdf
Data publikacji:
2012
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
domain market
domain registration
forecasting
time series modeling
Opis:
The paper presents analysis of the domain name reservation process for the polish .pl domain. Two models of various time scale are constructed and finally combined to build long range high resolution model. The results of prediction are verified using real data.
Źródło:
Journal of Telecommunications and Information Technology; 2012, 2; 63-73
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie szeregów czasowych ze składową periodyczną z wykorzystaniem pakietu TSprediction programu R
Forecasting time series with periodic component using TSprediction R package
Autorzy:
Bartłomowicz, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/424791.pdf
Data publikacji:
2014
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
forecasting
time-series methods
TSprediction package
R program
Opis:
The main aim of the paper is to present selected features of TSprediction package developed for R environment, which now is one of the most important commercial computing platforms (offered under the GNU GPL license). The article presents the features of the TSprediction package enabling the prediction of time series where there is a periodic component in the form of seasonal fluctuations. The package includes an implementation of the most popular time series methods of forecasting with a periodic component in the additive and multiplicative variety of ratio and Winters and Klein methods. The effects of selected forecasting functions and ex-post forecasting errors of TSprediction R package are presented in the examples.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2014, 4(46); 199-210
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Exponential smoothing and resampling techniques in time series prediction
Autorzy:
Neves, Maria
Cordeiro, Clara
Powiązania:
https://bibliotekanauki.pl/articles/729996.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:
time series
bootstrap
exponential smoothing
forecasting
accuracy measures
Opis:
Time series analysis deals with records that are collected over time. The objectives of time series analysis depend on the applications, but one of the main goals is to predict future values of the series. These values depend, usually in a stochastic manner, on the observations available at present. Such dependence has to be considered when predicting the future from its past, taking into account trend, seasonality and other features of the data. Some of the most successful forecasting methods are based on the concept of exponential smoothing. There are a variety of methods that fall into the exponential smoothing family, each having the property that forecasts are weighted combinations of past observations. But time series analysis needs proper statistical modeling. The model that better describes the behavior of the series in study can be crucial in obtaining 'good' forecasts. Departures from the true underlying distribution can adversely affect those forecasts. Resampling techniques have been considered in many situations to overcome that difficulty. For time series, several authors have proposed bootstrap methodologies. Here we will present an automatic procedure built in R language that first selects the best exponential smoothing model (among a set of possibilities) for fitting the data, followed by a bootstrap approach for obtaining forecasts. A real data set has been used to illustrate the performance of the proposed procedure.
Źródło:
Discussiones Mathematicae Probability and Statistics; 2010, 30, 1; 87-101
1509-9423
Pojawia się w:
Discussiones Mathematicae Probability and Statistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods
Autorzy:
Yolcu, Ufuk
Bas, Eren
Powiązania:
https://bibliotekanauki.pl/articles/633062.pdf
Data publikacji:
2016-06-01
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
fuzzy time series
forecasting
labour force participation
unemployment
Opis:
Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.
Źródło:
Comparative Economic Research. Central and Eastern Europe; 2016, 19, 2; 5-25
1508-2008
2082-6737
Pojawia się w:
Comparative Economic Research. Central and Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The prediction of new Covid-19 cases in Poland with machine learning models
Autorzy:
Chwila, Adam
Powiązania:
https://bibliotekanauki.pl/articles/14764465.pdf
Data publikacji:
2023-03-15
Wydawca:
Główny Urząd Statystyczny
Tematy:
machine learning
time series
COVID-19
forecasting
economic activity
Opis:
The COVID-19 pandemic has had a huge impact both on the global economy and on everyday life in all countries all over the world. In this paper, we propose several possible machine learning approaches to forecasting new confirmed COVID-19 cases, including the LASSO regression, Gradient Boosted (GB) regression trees, Support Vector Regression (SVR), and Long-Short Term Memory (LSTM) neural network. The above methods are applied in two variants: to the data prepared for the whole Poland and to the data prepared separately for each of the 16 voivodeships (NUTS 2 regions). The learning of all the models has been performed in two variants: with the 5-fold time-series cross-validation as well as with the split into the single train and test subsets. The computations in the study used official statistics from government reports from the period of April 2020 to March 2022. We propose a setup of 16 scenarios of the model selection to detect the model characterized by the best ex-post prediction accuracy. The scenarios differ from each other by the following features: the machine learning model, the method for the hyperparameters selection and the data setup. The most accurate scenario for the LASSO and SVR machine learning approaches is the single train/test dataset split with data for the whole Poland, while in case of the LSTM and GB trees it is the cross validation with data for whole Poland. Among the best scenarios for each model, the most accurate ex-post RMSE is obtained for the SVR. For the model performing best in terms of the ex-post RMSE, the interpretation of the outcome is conducted with the Shapley values. The Shapley values make it possible to present the impact of auxiliary variables in the machine learning model on the actual predicted value. The knowledge regarding factors that have the strongest impact on the number of new infections can help companies to plan their economic activity during turbulent times of pandemics. We propose to identify and compare the most important variables that affect both the train and test datasets of the model.
Źródło:
Statistics in Transition new series; 2023, 24, 2; 59-83
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie wypłat z bankomatów
Forecasting Withdrawals from ATMs
Autorzy:
Gurgul, Henryk
Suder, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/543001.pdf
Data publikacji:
2015
Wydawca:
Główny Urząd Statystyczny
Tematy:
Forecasting
Cash machine
Time-series
Forecasting models
Prognozowanie
Bankomaty
Szeregi czasowe
Modele prognostyczne
Opis:
Celem artykułu jest porównanie jakości prognoz zarówno ex post, jak i ex ante dotyczących zapotrzebowania na gotówkę w bankomatach, przy wykorzystaniu różnych metod prognozowania na podstawie szeregów czasowych wypłat. (fragment tekstu)
The authors explain links between strategy of replenishment of ATMs and costs of ATMs holders. Cost minimalization depends on accuracy of forecasts of withdrawals from ATMs. In the paper the several forecasting methods of withdrawals from ATMs in Euronet network installed in Małopolskie and Podkarpackie voivodships are applied. The used forecasting models are compared based on quality of ex post and ex ante forecasts. The model used in forecasting process depends on many factors e.g. location of ATM or calendar effects. The importance and role of these factors are analyzed in the paper. The authors supplied evidence, that suggested forecasts based on weighted averages are more accurate than forecasts based on methods applied by other authors. (original abstract)
Źródło:
Wiadomości Statystyczne. The Polish Statistician; 2015, 8; 25-48
0043-518X
Pojawia się w:
Wiadomości Statystyczne. The Polish Statistician
Dostawca treści:
Biblioteka Nauki
Artykuł
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