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Journal articles on the topic 'Time series'

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Author: Grafiati
Published: 4 June 2021
Last updated: 13 July 2024

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1

Cipra, Tomáš. "Asymmetric recursive methods for time series." Applications of Mathematics 39, no. 3 (1994): 203–14. http://dx.doi.org/10.21136/am.1994.134253.

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2

Ratinger, Tomáš. "Seasonal time series with missing observations." Applications of Mathematics 41, no. 1 (1996): 41–55. http://dx.doi.org/10.21136/am.1996.134312.

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3

CIUIU, Daniel. "STRICT STATIONARY TIME SERIES AND AUTOCOPULA." Review of the Air Force Academy 16, no. 2 (October 31, 2018): 53–58. http://dx.doi.org/10.19062/1842-9238.2018.16.2.6.

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4

Ray, W. D., Maurice Kendall, and J. K. Ord. "Time Series." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 2 (1994): 308. http://dx.doi.org/10.2307/2983371.

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5

Booth, David E., Maurice Kendall, and J. Keith Ord. "Time Series." Technometrics 34, no. 1 (February 1992): 118. http://dx.doi.org/10.2307/1269585.

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6

KK, Maurice Kendall, and J. Keith Ord. "Time Series." Journal of the American Statistical Association 90, no. 432 (December 1995): 1492. http://dx.doi.org/10.2307/2291552.

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7

KK and Andrew Harvey. "Time Series." Journal of the American Statistical Association 90, no. 432 (December 1995): 1493. http://dx.doi.org/10.2307/2291556.

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8

Ziegel, Eric R. "Time Series." Technometrics 44, no. 4 (November 2002): 408. http://dx.doi.org/10.1198/tech.2002.s95.

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9

Holmes, William M. "Time Series." International Journal of Forecasting 7, no. 4 (March 1992): 532–33. http://dx.doi.org/10.1016/0169-2070(92)90037-a.

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10

Lounds, W. S., M. Kendall, and J. K. Ord. "Time Series." Statistician 43, no. 3 (1994): 461. http://dx.doi.org/10.2307/2348592.

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11

Martin, R. J., M. G. Kendall, and J. K. Ord. "Time Series." Statistician 40, no. 4 (1991): 463. http://dx.doi.org/10.2307/2348750.

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12

Mitzev, Ivan S., and Nickolas H. Younan. "Time Series Shapelets: Training Time Improvement Based on Particle Swarm Optimization." International Journal of Machine Learning and Computing 5, no. 4 (August 2015): 283–87. http://dx.doi.org/10.7763/ijmlc.2015.v5.521.

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13

Xie, Wen-Jie, Rui-Qi Han, and Wei-Xing Zhou. "Time series classification based on triadic time series motifs." International Journal of Modern Physics B 33, no. 21 (August 20, 2019): 1950237. http://dx.doi.org/10.1142/s0217979219502370.

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It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series. Based on triadic time series motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We validate the method with time series generated from nonlinear dynamic systems (logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map) and retrieved from the UCR Time Series Classification Archive. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain datasets investigated in this work.
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14

Ramanujam, E., and S. Padmavathi. "Genetic time series motif discovery for time series classification." International Journal of Biomedical Engineering and Technology 31, no. 1 (2019): 47. http://dx.doi.org/10.1504/ijbet.2019.101051.

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15

Foster, Grant, and Patrick T. Brown. "Time and tide: analysis of sea level time series." Climate Dynamics 45, no. 1-2 (July 5, 2014): 291–308. http://dx.doi.org/10.1007/s00382-014-2224-3.

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16

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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17

Sokannit, Patcharakorn. "Forecasting Household Electricity Consumption Using Time Series Models." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 380–86. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1065.

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18

Panayotova, Galina S., and Dimitar A. Dimitrov. "Modeling from Time Series of Complex Brain Signals." International Journal of Signal Processing Systems 9, no. 1 (March 2021): 1–6. http://dx.doi.org/10.18178/ijsps.9.1.1-6.

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Signals obtained from most of real-world systems, especially from living organisms, are irregular, often chaotic, non-stationary, and noise-corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). In the paper given a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time series. Brain signals can be recorded by brainwave controlled applications, such as EMotiv Epoc +14. The paper investigates the models of the observed brain signals using time series, analyzes their applicability and develops new statistical models for their study.
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19

Dingli, Alexiei, and Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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20

Ahn. "Generation of blast load time series under tunnelling." Journal of Korean Tunnelling and Underground Space Associa 16, no. 1 (2014): 051. http://dx.doi.org/10.9711/ktaj.2014.16.1.051.

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21

Gruber, Christine, and Leopold Haimberger. "On the homogeneity of radiosonde wind time series." Meteorologische Zeitschrift 17, no. 5 (October 27, 2008): 631–43. http://dx.doi.org/10.1127/0941-2948/2008/0298.

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22

Salehi, M. R., E. Abiri, and L. Dehyadegari. "Nanophotonic Reservoir Computing for Noisy Time Series Classification." International Journal of Computer and Electrical Engineering 6, no. 3 (2014): 240–43. http://dx.doi.org/10.7763/ijcee.2014.v6.830.

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23

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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24

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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25

Souza, Reinaldo Castro. "PRACTICAL TIME SERIES." Pesquisa Operacional 21, no. 2 (July 2001): 219–21. http://dx.doi.org/10.1590/s0101-74382001000200006.

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26

Goulding, Gunilla. "Time Series Analyzer." Proceedings of the Water Environment Federation 2002, no. 11 (January 1, 2002): 557. http://dx.doi.org/10.2175/193864702784900156.

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27

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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28

Ziegel, Eric R., D. R. Cox, D. V. Hinkley, and O. E. Barndorff-nielsen. "Time Series Models." Technometrics 39, no. 1 (February 1997): 110. http://dx.doi.org/10.2307/1270795.

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29

Weiß, Christian H. "Time Series Modeling." Entropy 23, no. 9 (September 4, 2021): 1163. http://dx.doi.org/10.3390/e23091163.

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30

MAASKANT, JOLANDA, and BART LAAN. "Interrupted time series." TVZ - Verpleegkunde in praktijk en wetenschap 131, no. 4 (August 2021): 48–49. http://dx.doi.org/10.1007/s41184-021-0997-5.

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31

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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32

Ljung, Greta M., and Andrew C. Harvey. "Time Series Models." Journal of the American Statistical Association 90, no. 429 (March 1995): 394. http://dx.doi.org/10.2307/2291179.

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33

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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34

Thompson, D. B. A., Edward C. Mackey, T. M. Powell, and J. H. Steele. "Ecological Time Series." Journal of Ecology 84, no. 2 (April 1996): 322. http://dx.doi.org/10.2307/2261368.

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35

Chatfield, Chris. "Time-series forecasting." Significance 2, no. 3 (September 2005): 131–33. http://dx.doi.org/10.1111/j.1740-9713.2005.00117.x.

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36

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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37

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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38

Handley, Nicholas J. "Time Series Momentum." CFA Digest 42, no. 3 (August 2012): 179–81. http://dx.doi.org/10.2469/dig.v42.n3.47.

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39

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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40

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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41

Gabr, M. M., and L. M. Fatehy. "Time Series Classification." Journal of Statistics Applications & Probability 2, no. 2 (July 1, 2013): 123–33. http://dx.doi.org/10.12785/jsap/020205.

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42

Borkowf, Craig B. "Time-Series Forecasting." Technometrics 44, no. 2 (May 2002): 194–95. http://dx.doi.org/10.1198/tech.2002.s718.

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43

Sarkar, Pradipta. "Practical Time Series." Technometrics 44, no. 2 (May 2002): 195–96. http://dx.doi.org/10.1198/tech.2002.s719.

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44

Trindade, A. Alexandre. "Time-Series Forecasting." Journal of the American Statistical Association 97, no. 459 (September 2002): 920. http://dx.doi.org/10.1198/016214502760301192.

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45

McGee, Monnie. "Practical Time Series." Journal of the American Statistical Association 97, no. 457 (March 2002): 363–64. http://dx.doi.org/10.1198/jasa.2002.s461.

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46

"Exploring Time Series Randomness." Current Research in Statistics & Mathematics 3, no. 1 (April 22, 2024): 01–07. http://dx.doi.org/10.33140/crsm.03.01.06.

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Assessing the randomness within time series becomes challenging in the case of large-scale datasets. This novel approach leverages the efficiency of Locality Sensitive Hashing in detecting the repeating patterns over time as well as different time series. By breaking each time series down into pre-defined blocks, the solution set consists of pairs of similar blocks in accordance with the metric the proposed method approximates. As a consequence, the estimation of the aforementioned randomness turns into a pattern recognition problem, insofar as the more patterns are repeated over time, the more predictable the data becomes. Therefore, a simple measurement of the overall randomness of the time series in the input dataset is obtained by counting the identified similar blocks. Following the detection of similar patterns, the mutual information exchanged across the blocks of every detected pair is investigated to validate the results. A case study concerning a selection of different financial market indices is discussed to evaluate the potential of the proposed algorithm.
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47

Pfeifer, Phillip E. "Time Series." SSRN Electronic Journal, 2008. http://dx.doi.org/10.2139/ssrn.1284268.

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48

Boxall, Simon. "Time for Time Series." Oceanography 26, no. 2 (2013). http://dx.doi.org/10.5670/oceanog.2013.24.

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49

Kunz, Michael, Christoph Kottmeier, Wolfgang Lähne, Ingo Bertram, and Christian Ehmann. "The Karlsruhe temperature time series since 1779." Meteorologische Zeitschrift, January 1, 2022. http://dx.doi.org/10.1127/metz/2022/1106.

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50

Rezaee, Zabihollah. "Application of Time Series Analyses in Forensic Accounting." International Journal of Forensic Sciences 3, no. 3 (2018). http://dx.doi.org/10.23880/ijfsc-16000146.

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