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Artigos de revistas sobre o tema "Time-series analysis"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de julho de 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 (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 Decembe

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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 acc

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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 acc

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4

Bowerman, Bruce, and Jonathan D. Cryer. "Time Series Analysis." Technometrics 29, no. 2 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 vari

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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 (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 (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 (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 (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 (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 (1990): 501. http://dx.doi.org/10.2307/3172639.

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29

Campbell, Katherine. "Nonlinear Time Series Analysis." Technometrics 43, no. 4 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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) algo

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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 (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 (2004): 41–57. http://dx.doi.org/10.1007/bf03404608.

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46

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

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47

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

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48

Zhang, Nien Fan, D. C. Montgomery, L. A. Johnson, and J. S. Gardiner. "Forecasting and Time Series Analysis." Technometrics 34, no. 1 (1992): 98. http://dx.doi.org/10.2307/1269557.

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49

Ziegel, Eric R., and Chris Chatfield. "The Analysis of Time Series." Technometrics 39, no. 3 (1997): 342. http://dx.doi.org/10.2307/1271155.

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50

Valsakumar, M. C., K. P. N. Murthy, and S. Venkadesan. "Time Series Analysis - A Review." Solid State Phenomena 42-43 (April 1995): 141–54. http://dx.doi.org/10.4028/www.scientific.net/ssp.42-43.141.

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