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Journal articles on the topic 'Time series'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 July 2025

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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

Mehta, Aksh, Ayush Mattoo, and Dylan Pereira. "COVID Cases Prediction using Time Series Models." International Journal of Science and Research (IJSR) 11, no. 5 (May 5, 2022): 1148–56. http://dx.doi.org/10.21275/sr22512123708.

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14

P Sonalkar, V. "Export of Marine Product: Time Series Model." International Journal of Science and Research (IJSR) 10, no. 1 (January 27, 2021): 1019–22. https://doi.org/10.21275/sr21119080533.

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15

Lim, Aaron, Yejin Ha, Young-Chul Jang, and Jae-Hyung Cho. "Detecting Time-Series Data Anomalies with the Residual Wasserstein Time-Series GAN." Journal of the Korea Academia-Industrial cooperation Society 26, no. 1 (January 31, 2025): 1–8. https://doi.org/10.5762/kais.2025.26.1.1.

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16

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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17

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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18

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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19

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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20

Maus, Victor, Gilberto Camara, Ricardo Souza, Alber Sanchez, and Fernando Ramos. "A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 8 (February 11, 2016): 3729–39. https://doi.org/10.1109/JSTARS.2016.2517118.

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This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

ALIEN, MYLES R., PETER L. READ, and LEONARD A. SMITH. "Temperature time-series?" Nature 355, no. 6362 (February 1992): 686. http://dx.doi.org/10.1038/355686a0.

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43

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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44

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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45

Beukema, J. J. "Ecological time series." Journal of Experimental Marine Biology and Ecology 196, no. 1-2 (March 1996): 376–78. http://dx.doi.org/10.1016/s0022-0981(96)90037-4.

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46

Schreiber, Thomas, and Andreas Schmitz. "Surrogate time series." Physica D: Nonlinear Phenomena 142, no. 3-4 (August 2000): 346–82. http://dx.doi.org/10.1016/s0167-2789(00)00043-9.

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47

"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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48

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

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49

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

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50

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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