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Journal articles on the topic 'Time series data management'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Biem, A., H. Feng, A. V. Riabov, and D. S. Turaga. "Real-time analysis and management of big time-series data." IBM Journal of Research and Development 57, no. 3/4 (2013): 8:1–8:12. http://dx.doi.org/10.1147/jrd.2013.2243551.

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2

Mahaney, John K., N. A. Jr., David Lee Baker, James H. Hamburg, and David E. Booth. "Time series analysis of process data." International Journal of Operational Research 2, no. 3 (2007): 231. http://dx.doi.org/10.1504/ijor.2007.012851.

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3

Rasmussen, Rasmus. "On time series data and optimal parameters." Omega 32, no. 2 (2004): 111–20. http://dx.doi.org/10.1016/j.omega.2003.09.013.

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4

Zhou, Qifeng, Ruyuan Han, Tao Li, and Bin Xia. "Joint prediction of time series data in inventory management." Knowledge and Information Systems 61, no. 2 (2019): 905–29. http://dx.doi.org/10.1007/s10115-018-1302-y.

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5

Cuffe, Paul. "Playing Fair With Time Series Data." IEEE Potentials 39, no. 6 (2020): 47–50. http://dx.doi.org/10.1109/mpot.2018.2868000.

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6

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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7

Inniss, Tasha R. "Seasonal clustering technique for time series data." European Journal of Operational Research 175, no. 1 (2006): 376–84. http://dx.doi.org/10.1016/j.ejor.2005.03.049.

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8

Zhang, Kaimeng, Chi Tim Ng, and Myung Hwan Na. "Real time prediction of irregular periodic time series data." Journal of Forecasting 39, no. 3 (2020): 501–11. http://dx.doi.org/10.1002/for.2637.

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9

Patterson, K. D. "Exploiting information in vintages of time-series data." International Journal of Forecasting 19, no. 2 (2003): 177–97. http://dx.doi.org/10.1016/s0169-2070(01)00145-5.

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10

Welch, Eric, Stuart Bretschneider, and John Rohrbaugh. "Accuracy of judgmental extrapolation of time series data." International Journal of Forecasting 14, no. 1 (1998): 95–110. http://dx.doi.org/10.1016/s0169-2070(97)00055-1.

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11

Kuo, Huang Cheng, Tsung Lung Lee, and Jen Peng Huang. "Cluster analysis on time series gene expression data." International Journal of Business Intelligence and Data Mining 5, no. 1 (2010): 56. http://dx.doi.org/10.1504/ijbidm.2010.030299.

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12

Zhu, Ye, Yongjian Fu, and Huirong Fu. "On privacy-preserving time series data classification." International Journal of Data Mining, Modelling and Management 2, no. 2 (2010): 117. http://dx.doi.org/10.1504/ijdmmm.2010.032145.

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13

Chilingaryan, S., A. Beglarian, A. Kopmann, and S. Vöcking. "Advanced data extraction infrastructure: Web based system for management of time series data." Journal of Physics: Conference Series 219, no. 4 (2010): 042034. http://dx.doi.org/10.1088/1742-6596/219/4/042034.

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14

Singh, Lisa, and Mehmet Sayal. "Privately detecting bursts in streaming, distributed time series data." Data & Knowledge Engineering 68, no. 6 (2009): 509–30. http://dx.doi.org/10.1016/j.datak.2008.12.003.

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15

Pérez, Ana. "Comments on “Kernel density estimation for time series data”." International Journal of Forecasting 28, no. 1 (2012): 15–19. http://dx.doi.org/10.1016/j.ijforecast.2011.02.001.

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16

Angers, Jean-François, Atanu Biswas, and Raju Maiti. "Bayesian Forecasting for Time Series of Categorical Data." Journal of Forecasting 36, no. 3 (2016): 217–29. http://dx.doi.org/10.1002/for.2426.

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17

González-Rivera, Gloria, and Javier Arroyo. "Time series modeling of histogram-valued data: The daily histogram time series of S&P500 intradaily returns." International Journal of Forecasting 28, no. 1 (2012): 20–33. http://dx.doi.org/10.1016/j.ijforecast.2011.02.007.

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18

Wu, Kehe, Yayun Zhu, Quan Li, and Ziwei Wu. "A distributed real-time data prediction framework for large-scale time-series data using stream processing." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (2017): 145–65. http://dx.doi.org/10.1108/ijicc-09-2016-0033.

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Purpose The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand. Design/methodology/approach First, the autoregressive integrated moving average-based prediction algori

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19

Neill, Daniel B. "Expectation-based scan statistics for monitoring spatial time series data." International Journal of Forecasting 25, no. 3 (2009): 498–517. http://dx.doi.org/10.1016/j.ijforecast.2008.12.002.

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20

DePenya, F. J., and L. A. Gil-Alana. "Testing of nonstationary cycles in financial time series data." Review of Quantitative Finance and Accounting 27, no. 1 (2006): 47–65. http://dx.doi.org/10.1007/s11156-006-8542-8.

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21

Cancelo, Jose Ramon, and Antoni Espasa. "Using high-frequency data and time series models to improve yield management." International Journal of Services Technology and Management 2, no. 1/2 (2001): 59. http://dx.doi.org/10.1504/ijstm.2001.001591.

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22

Roadknight, Chris, Laura Parrott, Nathan Boyd, and Ian W. Marshall. "Real-Time Data Management on a Wireless Sensor Network." International Journal of Distributed Sensor Networks 1, no. 2 (2005): 215–25. http://dx.doi.org/10.1080/15501320590966468.

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A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more ‘hands off’ implementation which is demonstrated by a real world oceanographic deployment of the system.

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23

Veney, J. E. "Evaluation Applications of Regression Analysis with Time-series Data." American Journal of Evaluation 14, no. 3 (1993): 259–74. http://dx.doi.org/10.1177/109821409301400305.

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24

Saporta, G. "Data analysis for numerical and categorical individual time-series." Applied Stochastic Models and Data Analysis 1, no. 2 (1985): 109–19. http://dx.doi.org/10.1002/asm.3150010204.

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25

Lam, K. "Working's effect revisited—Fitting univariate time series to stock price data." Omega 18, no. 3 (1990): 337–38. http://dx.doi.org/10.1016/0305-0483(90)90046-c.

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26

Xiao, Zhiwen, and Jianbin Jiao. "Explainable Fraud Detection for Few Labeled Time Series Data." Security and Communication Networks 2021 (June 12, 2021): 1–9. http://dx.doi.org/10.1155/2021/9941464.

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Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed met

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27

Yao, Jiping, Puze Wang, Guoqiang Wang, Sangam Shrestha, Baolin Xue, and Wenchao Sun. "Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data." Science of The Total Environment 698 (January 2020): 134227. http://dx.doi.org/10.1016/j.scitotenv.2019.134227.

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28

Sen, Rituparna, and Claudia Klüppelberg. "Time series of functional data with application to yield curves." Applied Stochastic Models in Business and Industry 35, no. 4 (2019): 1028–43. http://dx.doi.org/10.1002/asmb.2443.

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29

Khosrowshahi, Farzad, and Amir M. Alani. "A model for smoothing time‐series data in construction." Construction Management and Economics 21, no. 5 (2003): 483–94. http://dx.doi.org/10.1080/0144619032000073541.

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30

Zolghadri, A., and D. Henry. "Minimax Statistical Models for Air Pollution Time Series. Application to Ozone Time Series Data Measured in Bordeaux." Environmental Monitoring and Assessment 98, no. 1-3 (2004): 275–94. http://dx.doi.org/10.1023/b:emas.0000038191.42255.7a.

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31

Hochin, Teruhisa, and Hiroki Nomiya. "Estimation of Daily Life Time Series Data Affected by Rainfall." International Journal of Engineering & Technology 7, no. 2.28 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.28.12885.

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The amount of sewage flow, which is one of daily life data, was estimated for their efficient management. The amounts of flow of a typical day were tried to be adjusted to those of a day. The values for the adjustment were tried to be estimated by using the multiple regression analysis. This method is applied to the estimation of the ammonia nitrogen concentration, which is the major factor of the quality of sewage flow. The estimation results show that this method is applicable to the estimation of the ammonia nitrogen concentration, and that the amount of rainfall is dominant in estimating t

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32

Chevallier, Julien, and Florian Ielpo. "“Time series momentum” in commodity markets." Managerial Finance 40, no. 7 (2014): 662–80. http://dx.doi.org/10.1108/mf-11-2013-0322.

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Purpose – The purpose of this paper is to contain an empirical application of the concept of “time series momentum” – as developed by Moskowitz et al. (2012) – to commodity markets with daily data during 1995-2012. Design/methodology/approach – The paper applies the new concept of “time series momentum” to the sphere of commodity markets. Findings – The paper extends the results previously obtained by Moskowitz et al. (2012) to a second category labeled “breakout strategy.” Research limitations/implications – Further management strategies can be elaborated for investment management purposes, b

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33

Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, et al. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.

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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and

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34

Darmawan, Didiet, Mohammad Isa Irawan, and Arie Dipareza Syafei. "Data Driven Analysis using Fuzzy Time Series for Air Quality Management in Surabaya." Sustinere: Journal of Environment and Sustainability 1, no. 2 (2017): 131–43. http://dx.doi.org/10.22515/sustinere.jes.v1i2.13.

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One of the environmental issues that can affect human health is air pollution. As the second largest city in Indonesia, economic development and infrastructure construction in the city of Surabaya led to the increasing role of industrial and motor vehicle use which is proportional to the increase in fuel oil consumption. This condition ultimately led to declining air quality. Gas pollutants that contribute to air pollution such as CO, SO2, O3, NO2 and particulate matter PM10 are pollutants that have a direct impact on health. This study aims to analyze, monitor and predict air pollutant concen

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35

Sadler, Jeffrey M., Daniel P. Ames, and Shaun J. Livingston. "Extending HydroShare to enable hydrologic time series data as social media." Journal of Hydroinformatics 18, no. 2 (2015): 198–209. http://dx.doi.org/10.2166/hydro.2015.331.

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The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) hydrologic information system (HIS) is a widely used service oriented system for time series data management. While this system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of ‘Web 2.0’ collaboration and social-networking capabilities being used in other fields. This paper presents the design, development, and testing of a software extension of CUAHSI's newest product, HydroShare. The extension integrates the existing CUAHSI

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36

Maier, Christian, Lorenz A. Kapsner, Sebastian Mate, Hans-Ulrich Prokosch, and Stefan Kraus. "Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model." Applied Clinical Informatics 12, no. 01 (2021): 057–64. http://dx.doi.org/10.1055/s-0040-1721481.

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Abstract Background The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interf

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37

Al-Osh, M. "A dynamic linear model approach for disaggregating time series data." Journal of Forecasting 8, no. 2 (1989): 85–96. http://dx.doi.org/10.1002/for.3980080203.

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38

Weng, Sung-Shun, and Yuan-Hung Liu. "Mining time series data for segmentation by using Ant Colony Optimization." European Journal of Operational Research 173, no. 3 (2006): 921–37. http://dx.doi.org/10.1016/j.ejor.2005.09.001.

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39

Wesonga, Ronald, Fabian Nabugoomu, and Brian Masimbi. "Airline Delay Time Series Differentials." International Journal of Aviation Systems, Operations and Training 1, no. 2 (2014): 64–76. http://dx.doi.org/10.4018/ijasot.2014070105.

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Flight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same

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40

Li, Hailin. "Time works well: Dynamic time warping based on time weighting for time series data mining." Information Sciences 547 (February 2021): 592–608. http://dx.doi.org/10.1016/j.ins.2020.08.089.

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41

Knijnenburg, T. A., L. F. A. Wessels, and M. J. T. Reinders. "Creating gene set activity profiles with time-series expression data." International Journal of Bioinformatics Research and Applications 4, no. 3 (2008): 306. http://dx.doi.org/10.1504/ijbra.2008.019577.

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42

Das, Rosy, Jugal Kalita, and Dhruba K. Bhattacharyya. "A new approach for clustering gene expression time series data." International Journal of Bioinformatics Research and Applications 5, no. 3 (2009): 310. http://dx.doi.org/10.1504/ijbra.2009.026422.

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43

Markellos, Raphael N., and Costas Siriopoulos. "Time-series Behavior of Intra-daily Data from the Athens Stock Exchange." International Transactions in Operational Research 9, no. 5 (2002): 619–28. http://dx.doi.org/10.1111/1475-3995.00377.

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44

Van Zyl-Bulitta, Verena Helen, R. Otte, and JH Van Rooyen. "Layer histogram patterns in financial time series." Corporate Ownership and Control 6, no. 3 (2009): 137–46. http://dx.doi.org/10.22495/cocv6i3p13.

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This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behavior of sequences of histograms. Histogram patterns in time adhere to several laws th

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45

Bou-Hamad, Imad, and Ibrahim Jamali. "Forecasting financial time-series using data mining models: A simulation study." Research in International Business and Finance 51 (January 2020): 101072. http://dx.doi.org/10.1016/j.ribaf.2019.101072.

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46

Walters, Carl J. "Bias in the Estimation of Functional Relationships from Time Series Data." Canadian Journal of Fisheries and Aquatic Sciences 42, no. 1 (1985): 147–49. http://dx.doi.org/10.1139/f85-018.

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Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for an

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47

Pei, Songwen, Tianma Shen, Xianrong Wang, et al. "3DACN: 3D Augmented convolutional network for time series data." Information Sciences 513 (March 2020): 17–29. http://dx.doi.org/10.1016/j.ins.2019.11.040.

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48

Storm, Scott M., Raymond R. Hill, and Joseph J. Pignatiello. "A Response Surface Methodology for Modeling Time Series Response Data." Quality and Reliability Engineering International 29, no. 5 (2012): 771–78. http://dx.doi.org/10.1002/qre.1427.

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49

Boori, M. S., K. Choudhary, and A. V. Kupriyanov. "Crop growth monitoring through Sentinel and Landsat data based NDVI time-series." Computer Optics 44, no. 3 (2020): 409–19. http://dx.doi.org/10.18287/2412-6179-co-635.

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Crop growth monitoring is an important phenomenon for agriculture classification, yield estimation, agriculture field management, improve productivity, irrigation, fertilizer management, sustainable agricultural development, food security and to understand how environment and climate change effect on crops especially in Russia as it has a large and diverse agricultural production. In this study, we assimilated monthly crop phenology from January to December 2018 by using the NDVI time series derived from moderate to high Spatio-temporal resolution Sentinel and Landsat data in cropland field at

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50

Zhou, Yanjun, Huorong Ren, Zhiwu Li, and Witold Pedrycz. "An anomaly detection framework for time series data: An interval-based approach." Knowledge-Based Systems 228 (September 2021): 107153. http://dx.doi.org/10.1016/j.knosys.2021.107153.

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