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Artykuły w czasopismach na temat „Time-series analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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1

Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (January 28, 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.

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2

Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "Predicting traffic congestion based on time series analysis." Sovremennaya nauka i innovatsii, no. 2 (42) (2023): 50–58. http://dx.doi.org/10.37493/2307-910x.2023.2.5.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.

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3

Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "PREDICTING TRAFFIC CONGESTION BASED ON TIME SERIES ANALYSIS." Sovremennaya nauka i innovatsii, no. 1 (41) (2023): 47–55. http://dx.doi.org/10.37493/2307-910x.2023.1.4.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion

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4

Bowerman, Bruce, and Jonathan D. Cryer. "Time Series Analysis." Technometrics 29, no. 2 (May 1987): 240. http://dx.doi.org/10.2307/1269781.

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5

Donatelli, Richard E., Ji-Ae Park, Spencer M. Mathews, and Shin-Jae Lee. "Time series analysis." American Journal of Orthodontics and Dentofacial Orthopedics 161, no. 4 (April 2022): 605–8. http://dx.doi.org/10.1016/j.ajodo.2021.07.013.

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6

Potscher, Benedikt M., and James D. Hamilton. "Time Series Analysis." Journal of the American Statistical Association 91, no. 433 (March 1996): 439. http://dx.doi.org/10.2307/2291435.

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7

Bakouch, Hassan S. "Time Series Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 1 (January 2009): 283. http://dx.doi.org/10.1111/j.1467-985x.2008.00571_4.x.

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8

Subba Rao, T. "Time Series Analysis." Journal of Time Series Analysis 31, no. 2 (March 2010): 139. http://dx.doi.org/10.1111/j.1467-9892.2009.00641.x.

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9

Breitung, Jorg, and James D. Hamilton. "Time Series Analysis." Contemporary Sociology 24, no. 2 (March 1995): 271. http://dx.doi.org/10.2307/2076916.

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10

Taylor, Diana. "Time-Series Analysis." Western Journal of Nursing Research 12, no. 2 (April 1990): 254–61. http://dx.doi.org/10.1177/019394599001200210.

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11

Mills, Terence C. "TIME SERIES ANALYSIS." Journal of Economic Surveys 9, no. 3 (September 1995): 325–28. http://dx.doi.org/10.1111/j.1467-6419.1995.tb00120.x.

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12

Dattalo, Patrick. "Time Series Analysis." Journal of Community Practice 5, no. 4 (September 30, 1998): 67–85. http://dx.doi.org/10.1300/j125v05n04_05.

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13

Bowermnn, Bruce. "Time Series Analysis." Technometrics 29, no. 2 (May 1987): 240. http://dx.doi.org/10.1080/00401706.1987.10488217.

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14

Booth, David E. "Time Series Analysis." Technometrics 39, no. 1 (February 1997): 102–3. http://dx.doi.org/10.1080/00401706.1997.10485448.

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15

Fildes, Robert. "Time series analysis." International Journal of Forecasting 2, no. 1 (January 1986): 117. http://dx.doi.org/10.1016/0169-2070(86)90037-3.

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16

Marquez, Jamie. "Time series analysis." International Journal of Forecasting 11, no. 3 (September 1995): 494–95. http://dx.doi.org/10.1016/0169-2070(95)90035-7.

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17

Gasser, T. "Time series analysis." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S221. http://dx.doi.org/10.1016/0013-4694(85)90839-9.

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18

Watson, Mark W., and Jonathan D. Cryer. "Time Series Analysis." Journal of the American Statistical Association 82, no. 400 (December 1987): 1195. http://dx.doi.org/10.2307/2289427.

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19

Militino, Ana F. "Time Series Analysis." Journal of Applied Statistics 36, no. 6 (June 2009): 699–700. http://dx.doi.org/10.1080/02664760802366809.

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20

LEDERMAN, REGINA P. "Time Series Analysis." MCN, The American Journal of Maternal/Child Nursing 18, no. 3 (May 1993): 177. http://dx.doi.org/10.1097/00005721-199305000-00013.

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21

Raftery, Adrian E. "Time series analysis." European Journal of Operational Research 20, no. 2 (May 1985): 127–37. http://dx.doi.org/10.1016/0377-2217(85)90052-9.

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22

M.M, Shah Khan, Akmal Anum, and Khan Hadiya. "Time Series Analysis of Role of Infrastructure in Growth of FDI." International Journal of Management Sciences and Business Research 4, no. 10 (October 31, 2015): 70–80. https://doi.org/10.5281/zenodo.3462897.

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Foreign Direct Investment (FDI) has its major contribution in economic development of a country. It has been clear from Literature that Foreign Direct Investment (FDI) not only facilitates in capital formation but also a source of new technology development in a country. This study has been conducted to ensure the impact of Infrastructure on FDI with the help of time series analysis by taking Pakistan as a case. The data of 30 years collected from two major sources; World Bank and Ministry of Finance for the time period 1980 to 2013. The variables used for this research are FDI (dependant variable) and Infrastructure (independent variable) also includes proxy variables with independent variable i-e; ((Real Government Expenditure per real GDP), Market size (Real GDP per capital), Openness (Real trade share (Import – export) per capital), Human Capital (total real education expenditure)Growth of FDI,

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23

Reddy Desani, Nithin. "Explainable AI for Time Series Analysis in Real - Time Supply Chain Optimization." International Journal of Science and Research (IJSR) 12, no. 1 (January 5, 2023): 1320–25. http://dx.doi.org/10.21275/es23110104518.

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24

Tsujimoto, Kazuki, and Toshiaki Omori. "Switching Probabilistic Slow Feature Analysis for Time Series Data." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 740–45. http://dx.doi.org/10.18178/ijmlc.2020.10.6.999.

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25

Thinsungnoen, Tippaya, Kittisak Kerdprasop, and Nittaya Kerdprasop. "A Deep Learning of Time Series for Efficient Analysis." International Journal of Future Computer and Communication 6, no. 3 (September 2017): 123–27. http://dx.doi.org/10.18178/ijfcc.2017.6.3.503.

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26

Kailash, Alle. "Performance Evaluation using Time Series Modeling." European Journal of Advances in Engineering and Technology 9, no. 11 (November 30, 2022): 110–22. https://doi.org/10.5281/zenodo.13353511.

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In this paper, we present an investigation into hidden patterns within customer data for a telecommunications company. Utilizing time-series analysis, we aim to uncover critical revenue trends for more accurate forecasting. Our findings will provide stakeholders with a deeper understanding of organizational revenue trends, enabling more informed decision-making and strategic planning.

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27

Perea, Jose A. "Topological Time Series Analysis." Notices of the American Mathematical Society 66, no. 05 (May 1, 2019): 1. http://dx.doi.org/10.1090/noti1869.

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28

Geurts, Michael D., and Lois W. Sayrs. "Pooled Time Series Analysis." Journal of Marketing Research 27, no. 4 (November 1990): 501. http://dx.doi.org/10.2307/3172639.

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29

Campbell, Katherine. "Nonlinear Time Series Analysis." Technometrics 43, no. 4 (November 2001): 491. http://dx.doi.org/10.1198/tech.2001.s50.

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30

Liu, Zonghua. "Chaotic Time Series Analysis." Mathematical Problems in Engineering 2010 (2010): 1–31. http://dx.doi.org/10.1155/2010/720190.

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Chaotic dynamical systems are ubiquitous in nature and most of them does not have an explicit dynamical equation and can be only understood through the available time series. We here briefly review the basic concepts of time series and its analytic tools, such as dimension, Lyapunov exponent, Hilbert transform, and attractor reconstruction. Then we discuss its applications in a few fields such as the construction of differential equations, identification of synchronization and coupling direction, coherence resonance, and traffic data analysis in Internet.

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31

Ziegel, Eric R. "Applied Time Series Analysis." Technometrics 32, no. 4 (November 1990): 451–52. http://dx.doi.org/10.1080/00401706.1990.10484736.

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32

Rycroft, Michael J. "Nonlinear Time Series Analysis." Journal of Atmospheric and Solar-Terrestrial Physics 62, no. 2 (January 2000): 152. http://dx.doi.org/10.1016/s1364-6826(99)00123-6.

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33

Kosmidis, Kosmas, Alkiviadis Kalampokis, and Panos Argyrakis. "Language time series analysis." Physica A: Statistical Mechanics and its Applications 370, no. 2 (October 2006): 808–16. http://dx.doi.org/10.1016/j.physa.2006.02.042.

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34

Bandt, Christoph. "Ordinal time series analysis." Ecological Modelling 182, no. 3-4 (March 2005): 229–38. http://dx.doi.org/10.1016/j.ecolmodel.2004.04.003.

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35

Grubb, Howard, and Lois W. Sayrs. "Pooled Time Series Analysis." Statistician 39, no. 1 (1990): 91. http://dx.doi.org/10.2307/2348206.

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36

Lasaponara, Rosa, and Antonio Lanorte. "Satellite time-series analysis." International Journal of Remote Sensing 33, no. 15 (February 13, 2012): 4649–52. http://dx.doi.org/10.1080/01431161.2011.638342.

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37

Bakouch, Hassan S. "Applied time series analysis." Journal of Applied Statistics 39, no. 10 (October 2012): 2300–2301. http://dx.doi.org/10.1080/02664763.2012.682445.

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38

Foster, Grant. "Time Series Analysis by Projection. II. Tensor Methods for Time Series Analysis." Astronomical Journal 111 (January 1996): 555. http://dx.doi.org/10.1086/117806.

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39

Tu, Min, Yuanjian Zhang, Jianfeng Xu, and Yu Li. "Analysis and Modeling of Time Series Based on Granular Computing." International Journal of Future Computer and Communication 4, no. 2 (April 2015): 93–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.363.

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40

Delima, Allemar Jhone P. "Application of Time Series Analysis in Projecting Philippines’ Electric Consumption." International Journal of Machine Learning and Computing 9, no. 5 (October 2019): 694–99. http://dx.doi.org/10.18178/ijmlc.2019.9.5.860.

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41

Anupriya and Anita Singhrova. "Comparative Analysis of Time Series Forecasting Models for SDMN Traffic." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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42

Huang, Guangdong, and Jiahong Li. "Hybrid Time Series Method for Long-Time Temperature Series Analysis." Discrete Dynamics in Nature and Society 2021 (July 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/9968022.

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This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.

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43

Lalley, Steven P., and A. B. Nobel. "Denoising deterministic time series." Dynamics of Partial Differential Equations 3, no. 4 (2006): 259–79. http://dx.doi.org/10.4310/dpde.2006.v3.n4.a1.

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44

Li, Ta-Hsin. "Time-correlation analysis of nonstationary time series." Journal of Time Series Analysis 19, no. 1 (January 1998): 47–67. http://dx.doi.org/10.1111/1467-9892.00076.

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45

Nachane, D. M. "Time-Frequency Analysis for Nonstationary Time Series." Journal of Quantitative Economics 2, no. 2 (July 2004): 41–57. http://dx.doi.org/10.1007/bf03404608.

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46

RAJASEKARAN, RAJKUMAR, GOVINDA K, ASHRITH REDDY, UDAY SAI REDDY, and YASHWANTH REDDY. "Visual Analysis of Temperature Time Series and Rainfall Using Big Data." Journal of Research on the Lepidoptera 50, no. 3 (September 25, 2019): 39–52. http://dx.doi.org/10.36872/lepi/v50i3/201023.

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47

Hanafiah, Mohd Hafiz Mohd, and Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis." International Journal of Trade, Economics and Finance 1, no. 1 (2010): 80–83. http://dx.doi.org/10.7763/ijtef.2010.v1.15.

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48

Hanafiah, Mohd Hafiz Mohd, and Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis." International Journal of Trade, Economics and Finance 1, no. 2 (2010): 200–203. http://dx.doi.org/10.7763/ijtef.2010.v1.36.

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Drozdov, I. Yu, and D. A. Petrusevich. "Water pollution time series analysis." IOP Conference Series: Materials Science and Engineering 1047, no. 1 (February 1, 2021): 012095. http://dx.doi.org/10.1088/1757-899x/1047/1/012095.

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Ziegel, Eric R., and T. S. Rao. "Developments in Time Series Analysis." Technometrics 37, no. 1 (February 1995): 127. http://dx.doi.org/10.2307/1269181.

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