<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>2ca386cd-7c07-4a56-8fd9-a15eb96d293c</doi_batch_id><timestamp>20250310111856183</timestamp><depositor><depositor_name>wseas:wseas</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS</full_title><issn media_type="electronic">2224-3402</issn><issn media_type="print">1790-0832</issn><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23209</doi><resource>http://wseas.org/wseas/cms.action?id=4046</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>1</month><day>7</day><year>2025</year></publication_date><publication_date media_type="print"><month>1</month><day>7</day><year>2025</year></publication_date><journal_volume><volume>22</volume><doi_data><doi>10.37394/23209.2025.22</doi><resource>https://wseas.com/journals/isa/2025.php</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Mathematical Model for Providing Remote Monitoring of Hardware and Software Complex at the Stage of Integration Testing</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Natalia</given_name><surname>Mamedova</surname><affiliation>Basic Department of Digital Economy, Plekhanov Russian University of Economics, 36, Stremyanny Lane, Moscow, 117997, RUSSIA</affiliation></person_name><person_name sequence="additional" contributor_role="author"><given_name>Timofey</given_name><surname>Bolonin</surname><affiliation>Basic Department of Digital Economy, Plekhanov Russian University of Economics, 36, Stremyanny Lane, Moscow, 117997, RUSSIA</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>This research is a contribution to the field of solutions for the effective process of technical hardware and software development. A mathematical model of providing remote monitoring of the hardware-software complex under development at the stage of integration testing is proposed. Monitoring of the hardware-software complex functioning in the process of development allows for leveling the limitations connected with resource-intensive integration testing and determining the influence of the developed parts of the complex (hardware and software) on the operability of the inherited functionality of the systems of the external environment. The data of mathematical modeling is proposed to be used to develop an emulator of the external environment systems of the developed hardware-software complex on a test bench and to conduct integration testing. The solution is a stochastic model since the subject of remote monitoring is defined as random events of the process of integration testing of the hardware-software complex. The mathematical model for time series modeling takes into account a set of metrics of hardware-software complex functioning and requirements for the future software implementation of the solution - the remote monitoring service being developed. The implementation of the mathematical model can be used by the IT developer when integrating monitoring data into the automated test system of hardware and software complex development.</jats:p></jats:abstract><publication_date media_type="online"><month>3</month><day>10</day><year>2025</year></publication_date><publication_date media_type="print"><month>3</month><day>10</day><year>2025</year></publication_date><pages><first_page>258</first_page><last_page>271</last_page></pages><publisher_item><item_number item_number_type="article_number">22</item_number></publisher_item><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2025-03-10"/><ai:license_ref applies_to="am" start_date="2025-03-10">https://wseas.com/journals/isa/2025/a425106-2105.pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/23209.2025.22.22</doi><resource>https://wseas.com/journals/isa/2025/a425106-2105.pdf</resource></doi_data><citation_list><citation key="ref0"><doi>10.1016/j.csi.2023.103744</doi><unstructured_citation>J. M. Alvarez-Rodríguez, R. Mendieta, E. Cibrián, and J. Llorens, “Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design,” Comput. Stand. Interfaces, vol. 86, p. 103744, 2023, https://doi.org/10.1016/j.csi.2023.103744. </unstructured_citation></citation><citation key="ref1"><doi>10.1016/j.oceaneng.2018.01.016</doi><unstructured_citation>L. O. Freire, L. M. Oliveira, R. T.S. Vale, M. Medeiros, R. E.Y. Diana, R. M. Lopes, E. L. Pellini, E. A. de Barros, “Development of an AUV control architecture based on systems engineering concepts,” Ocean Eng., vol. 151, pp. 157–169, 2018, https://doi.org/10.1016/j.oceaneng.2018.01. 016. </unstructured_citation></citation><citation key="ref2"><doi>10.1016/j.procs.2015.09.116</doi><unstructured_citation>A. Safwat and M. B. Senousy, “Addressing Challenges of Ultra Large Scale System on Requirements Engineering,” Procedia Comput. Sci., vol. 65, pp. 442–449, 2015, https://doi.org/10.1016/j.procs.2015.09.116. </unstructured_citation></citation><citation key="ref3"><doi>10.1016/b978-0-12-396525-7.00005-8</doi><unstructured_citation>M. F. Khan and R. A. Paul, “Chapter 4 - Pragmatic Directions in Engineering Secure Dependable Systems,” in Dependable and Secure Systems Engineering, vol. 84, A. Hurson and S. B. T.-A. in C. Sedigh, Eds. Elsevier, 2012, pp. 141–167, https://doi.org/10.1016/B978-0-12-396525- 7.00005-8. </unstructured_citation></citation><citation key="ref4"><doi>10.1016/0164-1212(94)00116-5</doi><unstructured_citation>L. R. Welch, A. L. Samuel, M. W. Masters, R. D. Harrison, M. Wilson, and J. Caruso, “Reengineering computer-based systems for enhanced concurrency and layering,” J. Syst. Softw., vol. 30, no. 1, pp. 45–70, 1995, https://doi.org/10.1016/0164- 1212(94)00116-5. </unstructured_citation></citation><citation key="ref5"><doi>10.1016/j.jksuci.2024.102094</doi><unstructured_citation>A. Ahmad, A. B. Altamimi, and J. Aqib, “A reference architecture for quantum computing as a service,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 6, p. 102094, 2024, https://doi.org/10.1016/j.jksuci.2024.102094 </unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.cogsys.2024.101286</doi><unstructured_citation>H. Beierling, P. Richter, M. Brandt, L. Terfloth, C. Schulte, H. Wersing, A.-L. Vollmer, “What you need to know about a learning robot: Identifying the enabling architecture of complex systems,” Cogn. Syst. Res., vol. 88, p. 101286, 2024, https://doi.org/10.1016/j.cogsys.2024.10128 6. </unstructured_citation></citation><citation key="ref7"><doi>10.1016/j.jii.2023.100457</doi><unstructured_citation>H. Loschi, D. Nascimento, R. Smolenski, and P. Lezynski, “Cyber–physical system for fast prototyping of power electronic converters in EMI shaping context,” J. Ind. Inf. Integr., vol. 33, p. 100457, 2023, https://doi.org/10.1016/j.jii.2023.100457. </unstructured_citation></citation><citation key="ref8"><doi>10.1016/j.fusengdes.2022.113357</doi><unstructured_citation>T. Zhang, Y. Shi, Y. Cheng, Y. Zeng, X. Zhang, and S. Liang, “The design and implementation of distributed architecture in the CMOR motion control system,” Fusion Eng. Des., vol. 186, p. 113357, 2023, https://doi.org/10.1016/j.fusengdes.2022.11 3357. </unstructured_citation></citation><citation key="ref9"><doi>10.1016/j.compag.2024.109251</doi><unstructured_citation>L. Yang, X. Li zhang, L. Yaoming, L. Liya, and S. Maolin, “Modeling and control methods of a multi-parameter system for threshing and cleaning in grain combine harvesters,” Comput. Electron. Agric., vol. 225, p. 109251, 2024, https://doi.org/10.1016/j.compag.2024.1092 51. </unstructured_citation></citation><citation key="ref10"><doi>10.1016/j.procs.2015.11.062</doi><unstructured_citation>P. Anistratov, Y. Golobokov, and V. Pavlov, “Hardware-software Complex Prototyping for the Pulse Power Supply Control System of Tokamak T-15,” Procedia Comput. Sci., vol. 66, pp. 546– 555, 2015, https://doi.org/10.1016/j.procs.2015.11.062. </unstructured_citation></citation><citation key="ref11"><doi>10.1016/j.procir.2017.03.013</doi><unstructured_citation>S. Wiesner, S. Nilsson, and K.-D. Thoben, “Integrating Requirements Engineering for Different Domains in System Development – Lessons Learnt from Industrial SME Cases,” Procedia CIRP, vol. 64, pp. 351– 356, 2017, https://doi.org/10.1016/j.procir.2017.03.013. </unstructured_citation></citation><citation key="ref12"><doi>10.1016/j.fusengdes.2008.01.016</doi><unstructured_citation>K. H. Kim, T. G. Lee, S. Baek, S. I. Lee, Y. Chu, Y. O. Kim, J. S. Kim, M. K. Park, Y. K. Oh, “Software development of the KSTAR Tokamak Monitoring System,” Fusion Eng. Des., vol. 83, no. 2, pp. 291– 294, 2008, https://doi.org/10.1016/j.fusengdes.2008.01. 016. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>F. Cus, M. Milfelner, and J. Balic, “An intelligent system for monitoring and optimization of ball-end milling process,” J. Mater. Process. Technol., vol. 175, no. 1, pp. 90–97, 2006, https://doi.org/10.1016/j.jmatprotec.2005.04 .041. </unstructured_citation></citation><citation key="ref14"><doi>10.1016/j.procs.2023.03.014</doi><unstructured_citation>S. Itaya, F. Ohori, T. Osuga, and T. Matsumura, “Smart Monitoring of Wireless Environments with Real-Time Aggregation and Analysis,” Procedia Comput. Sci., vol. 220, pp. 86–93, 2023, https://doi.org/10.1016/j.procs.2023.03.014. </unstructured_citation></citation><citation key="ref15"><doi>10.1016/0165-6074(93)90146-c</doi><unstructured_citation>J. P. Calvez and O. Pasquier, “Real-time behavior monitoring for multi-processor systems,” Microprocess. Microprogramming, vol. 38, no. 1, pp. 213– 220, 1993, https://doi.org/10.1016/0165- 6074(93)90146-C. </unstructured_citation></citation><citation key="ref16"><doi>10.1016/j.nucengdes.2010.03.011</doi><unstructured_citation>M. Lin, D. Hou, P. Liu, Z. Yang, and Y. Yang, “Main control system verification and validation of NPP digital I&amp;C system based on engineering simulator,” Nucl. Eng. Des., vol. 240, no. 7, pp. 1887–1896, 2010, https://doi.org/10.1016/j.nucengdes.2010.03 .011. </unstructured_citation></citation><citation key="ref17"><doi>10.1533/9781845696825.1.72</doi><unstructured_citation>R. A. Swartz and J. P. Lynch, “3 - Wireless sensors and networks for structural health monitoring of civil infrastructure systems,” in Woodhead Publishing Series in Civil and Structural Engineering, V. M. Karbhari and F. B. T.-S. H. M. of C. I. S. Ansari, Eds. Woodhead Publishing, 2009, pp. 72–112. </unstructured_citation></citation><citation key="ref18"><doi>10.1016/0165-6074(91)90334-p</doi><unstructured_citation>H. Honka and M. Kattilakoski, “A simulation-based system for testing realtime embedded software in the host environment,” Microprocess. Microprogramming, vol. 32, no. 1, pp. 127– 134, 1991, https://doi.org/10.1016/0165- 6074(91)90334-P. </unstructured_citation></citation><citation key="ref19"><doi>10.1016/j.procir.2017.01.018</doi><unstructured_citation>J. Van Noten, K. Gadeyne, and M. Witters, “Model-based Systems Engineering of Discrete Production Lines Using SysML: An Experience Report,” Procedia CIRP, vol. 60, pp. 157–162, 2017, https://doi.org/10.1016/j.procir.2017.01.018. </unstructured_citation></citation><citation key="ref20"><doi>10.1016/j.sysarc.2023.102928</doi><unstructured_citation>M. Foughali, P.-E. Hladik, and A. Zuepke, “Compositional verification of embedded real-time systems,” J. Syst. Archit., vol. 142, p. 102928, 2023, https://doi.org/10.1016/j.sysarc.2023.10292 8. </unstructured_citation></citation><citation key="ref21"><doi>10.1016/j.sciaf.2023.e01846</doi><unstructured_citation>A. EL Zerk, M. Ouassaid, and Y. Zidani, “Development of a real-time framework between MATLAB and PLC through OPCUA: A case study of a microgrid energy management system,” Sci. African, vol. 21, p. e01846, 2023, https://doi.org/10.1016/j.sciaf.2023.e01846. </unstructured_citation></citation><citation key="ref22"><doi>10.1016/j.matt.2021.06.036</doi><unstructured_citation>E. Stach, B. DeCost, A. G. Kusne, J. Hattrick-Simpers, K. A. Brown, K. G. Reyes, J. Schrier, S. Billinge, T. Buonassisi, I. Foster, C. P. Gomes, J. M. Gregoire, A. Mehta, J. Montoya, E. Olivetti, Ch. Park, E. Rotenberg, S. K. Saikin, S. Smullin, V. Stanev, B. Maruyama, “Autonomous experimentation systems for materials development: A community perspective,” Matter, vol. 4, no. 9, pp. 2702–2726, 2021, https://doi.org/10.1016/j.matt.2021.06.036. </unstructured_citation></citation><citation key="ref23"><doi>10.1016/j.cola.2020.101021</doi><unstructured_citation>E. de Araújo Silva, E. Valentin, J. R. H. Carvalho, and R. da Silva Barreto, “A survey of Model Driven Engineering in robotics,” J. Comput. Lang., vol. 62, p. 101021, 2021, https://doi.org/10.1016/j.cola.2020.101021. </unstructured_citation></citation><citation key="ref24"><doi>10.1016/j.sysarc.2024.103080</doi><unstructured_citation>E. Hussein, B. Waschneck, and C. Mayr, “Automating application-driven customization of ASIPs: A survey,” J. Syst. Archit., vol. 148, p. 103080, 2024, https://doi.org/10.1016/j.sysarc.2024.10308 0. </unstructured_citation></citation><citation key="ref25"><doi>10.1016/j.micpro.2018.07.007</doi><unstructured_citation>D. Zoni, L. Cremona, A. Cilardo, M. Gagliardi, and W. Fornaciari, “PowerTap: All-digital power meter modeling for runtime power monitoring,” Microprocess. Microsyst., vol. 63, pp. 128–139, 2018, https://doi.org/10.1016/j.micpro.2018.07.00 7. </unstructured_citation></citation><citation key="ref26"><doi>10.1016/b978-0-12-818880-4.00019-3</doi><unstructured_citation>A. Askhedkar, B. Chaudhari, and M. Zennaro, “18 - Hardware and software platforms for low-power wide-area networks,” B. S. Chaudhari and M. B. T.-L. T. for I. and M. A. Zennaro, Eds. Academic Press, 2020, pp. 397–407. https://doi.org/10.1016/B978-0-12-818880- 4.00019-3. </unstructured_citation></citation><citation key="ref27"><doi>10.1016/j.asoc.2014.08.021</doi><unstructured_citation>R. Ligeiro, “Monitoring applications: An immune inspired algorithm for softwarefault detection,” Appl. Soft Comput., vol. 24, pp. 1095–1104, 2014, https://doi.org/10.1016/j.asoc.2014.08.021. </unstructured_citation></citation><citation key="ref28"><doi>10.1016/s0736-5853(05)80060-7</doi><unstructured_citation>M. J. Ringer, T. M. Quinn, and A. Merolla, “Autonomous power system: Intelligent diagnosis and control,” Telemat. Informatics, vol. 8, no. 4, pp. 365–383, 1991, https://doi.org/10.1016/S0736- 5853(05)80060-7. </unstructured_citation></citation><citation key="ref29"><doi>10.1016/j.iot.2022.100617</doi><unstructured_citation>P. Charalampidis, A. Makrogiannakis, N. Karamolegkos, S. Papadakis, Y. Charalambakis, G. Kamaratakis, A. Fragkiadakis, “A flexible Compilation-as-aService and Remote-Programming-as-aService platform for IoT devices,” Internet of Things, vol. 20, p. 100617, 2022, https://doi.org/10.1016/j.iot.2022.100617. </unstructured_citation></citation><citation key="ref30"><doi>10.1016/j.procir.2024.07.022</doi><unstructured_citation>I. Heider, J. Baumgärtner, A. Bott, R. Ströbel, A. Puchta, and J. Fleischer, “Towards a Testing Framework for Machine Learning Model Deployment in Manufacturing Systems,” Procedia CIRP, vol. 127, pp. 122–128, 2024, https://doi.org/10.1016/j.procir.2024.07.022. </unstructured_citation></citation><citation key="ref31"><doi>10.1016/j.jss.2024.112202</doi><unstructured_citation>A. Lönnfält, V. Tu, G. Gay, A. Singh, and S. Tahvili, “An intelligent test management system for optimizing decision making during software testing,” J. Syst. Softw., vol. 219, p. 112202, 2025, https://doi.org/10.1016/j.jss.2024.112202. </unstructured_citation></citation><citation key="ref32"><doi>10.1016/j.micpro.2020.103072</doi><unstructured_citation>R. Seyyedi, S. Schreiner, M. Fakih, K. Grüttner, and W. Nebel, “Functional test environment for time-triggered control systems in complex MPSoCs,” Microprocess. Microsyst., vol. 76, p. 103072, 2020, https://doi.org/10.1016/j.micpro.2020.10307 2. </unstructured_citation></citation><citation key="ref33"><doi>10.1016/s1474-6670(17)43770-0</doi><unstructured_citation>R. Pitschinetz and J. Wegener, “TESSY - Management of Software Tests,” IFAC Proc. Vol., vol. 29, no. 2, pp. 11–16, 1996, https://doi.org/10.1016/S1474- 6670(17)43770-0. </unstructured_citation></citation><citation key="ref34"><doi>10.1016/j.infsof.2006.11.002</doi><unstructured_citation>S. Ali, L. C. Briand, M. J. Rehman, H. Asghar, M. Z. Z. Iqbal, and A. Nadeem, “A state-based approach to integration testing based on UML models,” Inf. Softw. Technol., vol. 49, no. 11, pp. 1087–1106, 2007, https://doi.org/10.1016/j.infsof.2006.11.002. </unstructured_citation></citation><citation key="ref35"><doi>10.1016/j.jss.2011.02.034</doi><unstructured_citation>F. Mattiello-Francisco, E. Martins, A. R. Cavalli, and E. T. Yano, “InRob: An approach for testing interoperability and robustness of real-time embedded software,” J. Syst. Softw., vol. 85, no. 1, pp. 3–15, 2012, https://doi.org/10.1016/j.jss.2011.02.034. </unstructured_citation></citation><citation key="ref36"><doi>10.1016/j.infsof.2022.107133</doi><unstructured_citation>Y. Ding, Y. Zhang, G. Yuan, S. Jiang, and W. Dai, “Progress on class integration test order generation approaches: A systematic literature review,” Inf. Softw. Technol., vol. 156, p. 107133, 2023, https://doi.org/10.1016/j.infsof.2022.107133 </unstructured_citation></citation><citation key="ref37"><doi>10.1016/j.jss.2022.111259</doi><unstructured_citation>Y. Wang, M. V Mäntylä, Z. Liu, and J. Markkula, “Test automation maturity improves product quality—Quantitative study of open source projects using continuous integration,” J. Syst. Softw., vol. 188, p. 111259, 2022, https://doi.org/10.1016/j.jss.2022.111259. </unstructured_citation></citation><citation key="ref38"><doi>10.1016/j.jss.2019.110421</doi><unstructured_citation>F. Trautsch, S. Herbold, and J. Grabowski, “Are unit and integration test definitions still valid for modern Java projects? An empirical study on open-source projects,” J. Syst. Softw., vol. 159, p. 110421, 2020, https://doi.org/10.1016/j.jss.2019.110421. </unstructured_citation></citation><citation key="ref39"><doi>10.1016/b978-0-323-90240-3.00006-0</doi><unstructured_citation>S. S. Yadav, A. Kumar, P. Johri, and J. N. Singh, “Chapter 6 - Testing effortdependent software reliability growth model using time lag functions under distributed environment,” in Emerging Methodologies and Applications in Modelling, P. Johri, A. Anand, J. Vain, J. Singh, and M. B. T.-S. A. Quasim, Eds. Academic Press, 2022, pp. 85–102. </unstructured_citation></citation><citation key="ref40"><doi>10.1016/j.istr.2008.03.002</doi><unstructured_citation>F. Saglietti, N. Oster, and F. Pinte, “White and grey-box verification and validation approaches for safety- and security-critical software systems,” Inf. Secur. Tech. Rep., vol. 13, no. 1, pp. 10–16, 2008, https://doi.org/10.1016/j.istr.2008.03.002. </unstructured_citation></citation><citation key="ref41"><doi>10.1016/j.compind.2010.05.011</doi><unstructured_citation>N. C. W. M. Braspenning, R. Boumen, J. M. van de Mortel-Fronczak, and J. E. Rooda, “Estimating and quantifying the impact of using models for integration and testing,” Comput. Ind., vol. 62, no. 1, pp. 65–77, 2011, https://doi.org/10.1016/j.compind.2010.05.0 11. </unstructured_citation></citation><citation key="ref42"><doi>10.1016/j.segan.2024.101355</doi><unstructured_citation>E. Losi, L. Manservigi, P. R. Spina, and M. Venturini, “Data-driven approach for the detection of faults in district heating networks,” Sustain. Energy, Grids Networks, vol. 38, p. 101355, 2024, https://doi.org/10.1016/j.segan.2024.101355 </unstructured_citation></citation><citation key="ref43"><doi>10.1016/j.oceaneng.2020.107174</doi><unstructured_citation>Y. Tan, H. Tian, R. Jiang, Y. Lin, and J. Zhang, “A comparative investigation of data-driven approaches based on one-class classifiers for condition monitoring of marine machinery system,” Ocean Eng., vol. 201, p. 107174, 2020, https://doi.org/10.1016/j.oceaneng.2020.107 174. </unstructured_citation></citation><citation key="ref44"><doi>10.1016/j.compind.2024.104086</doi><unstructured_citation>Y. Xu, Y. Qamsane, S. Puchala, A. Januszczak, D. M. Tilbury, and K. Barton, “A data-driven approach toward a machineand system-level performance monitoring digital twin for production lines,” Comput. Ind., vol. 157–158, p. 104086, 2024, https://doi.org/10.1016/j.compind.2024.104 086. </unstructured_citation></citation><citation key="ref45"><doi>10.1016/j.dche.2021.100009</doi><unstructured_citation>A. Puliyanda, K. Srinivasan, K. Sivaramakrishnan, and V. Prasad, “A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems,” Digit. Chem. Eng., vol. 2, p. 100009, 2022, https://doi.org/10.1016/j.dche.2021.100009. </unstructured_citation></citation><citation key="ref46"><doi>10.1016/j.promfg.2015.07.249</doi><unstructured_citation>S. Hamdan and S. Alramouni, “A Quality Framework for Software Continuous Integration,” Procedia Manuf., vol. 3, pp. 2019–2025, 2015, https://doi.org/10.1016/j.promfg.2015.07.24 9. </unstructured_citation></citation><citation key="ref47"><doi>10.1016/j.jss.2020.110614</doi><unstructured_citation>Y. Li, J. Wang, Y. Yang, and Q. Wang, “An extensive study of class-level and methodlevel test case selection for continuous integration,” J. Syst. Softw., vol. 167, p. 110614, 2020, https://doi.org/10.1016/j.jss.2020.110614. </unstructured_citation></citation><citation key="ref48"><unstructured_citation>S. M. Krone, “Spatial models: stochastic and deterministic,” Math. Comput. Model., vol. 40, no. 3, pp. 393–409, 2004, https://doi.org/10.1016/j.mcm.2003.09.037. </unstructured_citation></citation><citation key="ref49"><unstructured_citation>M. Voskoglou, “3.7 - A Stochastic Model for the Modelling Process,” C. Haines, P. Galbraith, W. Blum, and S. B. T.-M. M. Khan, Eds. Woodhead Publishing, 2007, pp. 149–157. </unstructured_citation></citation><citation key="ref50"><doi>10.1016/j.asoc.2014.05.028</doi><unstructured_citation>C. N. Babu and B. E. Reddy, “A movingaverage filter based hybrid ARIMA–ANN model for forecasting time series data,” Appl. Soft Comput., vol. 23, pp. 27–38, 2014, https://doi.org/10.1016/j.asoc.2014.05.028. </unstructured_citation></citation><citation key="ref51"><doi>10.1016/s0925-2312(01)00702-0</doi><unstructured_citation>G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159- 175, 2003, https://doi.org/10.1016/S0925- 2312(01)00702-0. </unstructured_citation></citation><citation key="ref52"><doi>10.1016/j.techfore.2010.01.009</doi><unstructured_citation>C. Christodoulos, C. Michalakelis, and D. Varoutas, “Forecasting with limited data: Combining ARIMA and diffusion models,” Technol. Forecast. Soc. Change, vol. 77, no. 4, pp. 558–565, 2010, https://doi.org/10.1016/j.techfore.2010.01.0 09.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>