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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 (May 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 (April 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 (January 1, 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 (November 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 (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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Inniss, Tasha R. "Seasonal clustering technique for time series data." European Journal of Operational Research 175, no. 1 (November 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 (January 6, 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 (April 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 (March 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 (April 1, 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 (June 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 (January 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 (May 9, 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 (January 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 (June 12, 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 algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework. Findings Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable. Originality/value This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

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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 (July 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 (August 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 (March 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 (October 1, 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 (January 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 method. Experimental results show that our proposed method achieves the similar predictive performance as the state-of-art method, while our method can generate clear causal explanations for a few labeled time series data. The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons for rejecting transactions so as to reduce fraud losses and management costs.

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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 (March 18, 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 (July 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 (November 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 (May 16, 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 the ammonia nitrogen concentration.

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32

Chevallier, Julien, and Florian Ielpo. "“Time series momentum” in commodity markets." Managerial Finance 40, no. 7 (June 3, 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, based on the suggested inclusion of the “time series momentum” in commodities. Practical implications – The empirical evidence gathered in this paper bears practical significance for portfolio managers and commodity tradings advisors relying on trend following strategies. Originality/value – Commodity markets are quickly developing to an alternative asset class for investors. Discovering their properties and characteristics has a broad appeal in finance.

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33

Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, Paulo Beltrão, Paulo Santos Junior, Glauber Marques, Otavio Chase, and Pedro Campos. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (August 1, 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 Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

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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 (December 29, 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 concentrations recorded by the Environment Agency Surabaya City based on time series with Fuzzy Time Series.MAPE calculation results on the parameters of pollutants are NO2: 23.6%, CO: 19.5%, O3: 22.75%, PM10: 9.96% and SO2: 3.6%.

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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 (November 27, 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 HIS into HydroShare's social hydrology architecture. With this extension, HydroShare provides integrated HIS time series with efficient archiving, discovery, and retrieval of the data, extensive creator and science metadata, scientific discussion and collaboration around the data and other basic social media features. HydroShare provides functionality for online social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The extension is expected to enable scientists to access and share both national- and laboratory-scale hydrologic time series datasets in a standards-based web services architecture combined with social media functionality developed specifically for the hydrologic sciences.

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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 (January 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 interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.

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37

Al-Osh, M. "A dynamic linear model approach for disaggregating time series data." Journal of Forecasting 8, no. 2 (April 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 (September 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 (July 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 airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.

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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 (September 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 that could not be detected when using time series analysis methods. In this study special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states.

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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 (January 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 any particular case by Monte Carlo simulations.

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47

Pei, Songwen, Tianma Shen, Xianrong Wang, Chunhua Gu, Zhong Ning, Xiaochun Ye, and Naixue Xiong. "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 (August 13, 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 (June 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 Samara airport area, Russia. The results support the potential of Sentinel and Landsat data derived NDVI time series for accurate crop phenological monitoring with all crop growth stages such as active tillering, jointing, maturity and harvesting according to crop calendar with reasonable thematic accuracy. This satellite data generated NDVI based work has great potential to provide valuable support for assessing crop growth status and the above-mentioned objectives with sustainable agriculture development.

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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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