{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:27:33Z","timestamp":1775032053122,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03060-4","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T16:02:16Z","timestamp":1720195336000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Enhancing Forecasting Accuracy with a Moving Average-Integrated Hybrid ARIMA-LSTM Model"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1368-4993","authenticated-orcid":false,"given":"Sumalatha","family":"Saleti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lovely Yeswanth","family":"Panchumarthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yogeshvar Reddy","family":"Kallam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lavanya","family":"Parchuri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa","family":"Jitte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"3060_CR1","unstructured":"Granger C, Newbold P. Forecasting economic time series 1986."},{"key":"3060_CR2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling P, Agon C. Time-series data mining. ACM Comput Surv (CSUR). 2012;45:12. https:\/\/doi.org\/10.1145\/2379776.2379788.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3060_CR3","doi-asserted-by":"crossref","unstructured":"Guha B, Bandyopadhyay G. Gold price forecasting using arima model. J Adv Manag Sci 2016. https:\/\/doi.org\/10.12720\/joams.4.2.117-121","DOI":"10.12720\/joams.4.2.117-121"},{"key":"3060_CR4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1080\/07350015.1995.10524598","volume":"13","author":"F Canova","year":"1995","unstructured":"Canova F, Hansen BE. Are seasonal patterns constant over time? A test for seasonal stability. J Bus Econ Stat. 1995;13:237\u201352.","journal-title":"J Bus Econ Stat"},{"key":"3060_CR5","doi-asserted-by":"publisher","unstructured":"Shumway R, Stoffer D. Time series analysis and its applications with R examples vol. 9, 2011; https:\/\/doi.org\/10.1007\/978-1-4419-7865-3.","DOI":"10.1007\/978-1-4419-7865-3"},{"key":"3060_CR6","doi-asserted-by":"crossref","unstructured":"Lin T, Horne W, Giles C. How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Netw 11;1998.","DOI":"10.1016\/S0893-6080(98)00018-5"},{"key":"3060_CR7","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/BF02294361","volume":"52","author":"H Bozdogan","year":"1987","unstructured":"Bozdogan H. Model selection and akaike\u2019s information criterion (aic): the general theory and its analytical extensions. Psychometrika. 1987;52:345\u201370. https:\/\/doi.org\/10.1007\/BF02294361.","journal-title":"Psychometrika"},{"key":"3060_CR8","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1177\/0049124104268644","volume":"33","author":"K Burnham","year":"2004","unstructured":"Burnham K. Understanding aic and bic in model selection. Sociol Methods Res. 2004;33:93. https:\/\/doi.org\/10.1177\/0049124104268644.","journal-title":"Sociol Methods Res"},{"key":"3060_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980030312","author":"E McKenzie","year":"1984","unstructured":"McKenzie E. General exponential smoothing and the equivalent arma process. J Forecast. 1984. https:\/\/doi.org\/10.1002\/for.3980030312.","journal-title":"J Forecast"},{"key":"3060_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.02.006","author":"W Xu","year":"2019","unstructured":"Xu W, Peng H, Zeng X, Zhou F, Tian X, Peng X. Deep belief network-based ar model for 3 nonlinear time series forecasting. Appl Soft Comput. 2019. https:\/\/doi.org\/10.1016\/j.asoc.2019.02.006.","journal-title":"Appl Soft Comput"},{"key":"3060_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.002","author":"H Kim","year":"2018","unstructured":"Kim H, Won C. Forecasting the volatility of stock price index: a hybrid model integrating lstm with multiple garch-type models. Expert Syst Appl. 2018. https:\/\/doi.org\/10.1016\/j.eswa.2018.03.002.","journal-title":"Expert Syst Appl"},{"key":"3060_CR12","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1080\/00401706.1985.10488076","volume":"27","author":"A Vecchia","year":"1985","unstructured":"Vecchia A. Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics. 1985;27:375\u201384. https:\/\/doi.org\/10.1080\/00401706.1985.10488076.","journal-title":"Technometrics"},{"key":"3060_CR13","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.5.4.374","author":"Z Tang","year":"1993","unstructured":"Tang Z, Fishwick P. Feedforward neural nets as models for time series forecasting. INFORMS J Comput. 1993. https:\/\/doi.org\/10.1287\/ijoc.5.4.374.","journal-title":"INFORMS J Comput"},{"key":"3060_CR14","doi-asserted-by":"publisher","DOI":"10.3846\/20294913.2016.1216906","author":"A Raudys","year":"2018","unstructured":"Raudys A, Pabarskite Z. Optimising the smoothness and accuracy of moving average for stock price data. Technol Econ Dev Econ. 2018. https:\/\/doi.org\/10.3846\/20294913.2016.1216906.","journal-title":"Technol Econ Dev Econ"},{"key":"3060_CR15","doi-asserted-by":"publisher","unstructured":"Kalpakis K, Gada D, Puttagunta V. Distance measures for effective clustering of arima time-series. 2001. https:\/\/doi.org\/10.1109\/ICDM.2001.989529.","DOI":"10.1109\/ICDM.2001.989529"},{"key":"3060_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0223593","author":"C Stoean","year":"2019","unstructured":"Stoean C, Paja W, Stoean R, Sandita A. Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLOS One. 2019. https:\/\/doi.org\/10.1371\/journal.pone.0223593.","journal-title":"PLOS One"},{"key":"3060_CR17","unstructured":"Kongcharoen C, Kruangpradit T. Autoregressive integrated moving average with explanatory variable (arimax) model for Thailand export. 2013."},{"key":"3060_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.12.172","author":"W Anggraini","year":"2015","unstructured":"Anggraini W, Vinarti R, Kurniawati Y. Performance comparisons between arima and arimax method in moslem kids clothes demand forecasting: case study. Procedia Comput Sci. 2015. https:\/\/doi.org\/10.1016\/j.procs.2015.12.172.","journal-title":"Procedia Comput Sci"},{"key":"3060_CR19","doi-asserted-by":"publisher","unstructured":"Mehtab S, Sen J, Dasgupta S. Robust analysis of stock price time series using cnn and lstm-based deep learning models. 2020. https:\/\/doi.org\/10.1109\/ICECA49313.2020.9297652.","DOI":"10.1109\/ICECA49313.2020.9297652"},{"key":"3060_CR20","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"P Zhang","year":"2003","unstructured":"Zhang P, Zhang GP. Time series forecasting using a hybrid arima and neural network model. Neurocomputing. 2003;50:159\u201375. https:\/\/doi.org\/10.1016\/S0925-2312(01)00702-0.","journal-title":"Neurocomputing"},{"key":"3060_CR21","unstructured":"Siami\u00a0Namini S, Tavakoli N, Siami\u00a0Namin A. A comparative analysis of forecasting financial time series using arima, lstm, and bilstm 2019."},{"key":"3060_CR22","first-page":"65","volume":"3","author":"S Nanayakkara","year":"2014","unstructured":"Nanayakkara S, Chandrasekara V, Jayasundara M. Forecasting exchange rates using time series and neural network approaches. Eur Int J Sci Technol. 2014;3:65\u201373.","journal-title":"Eur Int J Sci Technol"},{"key":"3060_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/S0305-0483(01)00022-6","author":"H Hwarng","year":"2001","unstructured":"Hwarng H. Insights into neural-network forecasting of time series corresponding to arma(p, q) structures. Omega. 2001. https:\/\/doi.org\/10.1016\/S0305-0483(01)00022-6.","journal-title":"Omega"},{"key":"3060_CR24","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2018.930.938","author":"A Salman","year":"2018","unstructured":"Salman A, Heryadi Y, Abdurahman E, Suparta W. Weather forecasting using merged long short-term memory model (lstm) and autoregressive integrated moving average (arima) model. J Comput Sci. 2018. https:\/\/doi.org\/10.3844\/jcssp.2018.930.938.","journal-title":"J Comput Sci"},{"key":"3060_CR25","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2600823","author":"F Johnston","year":"1999","unstructured":"Johnston F, Boyland J, Meadows M, Shale E. Some properties of a simple moving average when applied to forecasting a time series. J Oper Res Soc. 1999. https:\/\/doi.org\/10.1057\/palgrave.jors.2600823.","journal-title":"J Oper Res Soc"},{"key":"3060_CR26","doi-asserted-by":"publisher","unstructured":"Zhuangt Y, Chen L, Wang X, Lian J. A weighted moving average-based approach for cleaning sensor data. https:\/\/doi.org\/10.1109\/ICDCS.2007.83","DOI":"10.1109\/ICDCS.2007.83"},{"key":"3060_CR27","doi-asserted-by":"publisher","DOI":"10.1017\/S0021900200104693","author":"A Lawrance","year":"1977","unstructured":"Lawrance A, Lewis P. An exponential moving-average sequence and point process (ema1). J Appl Probabil. 1977. https:\/\/doi.org\/10.1017\/S0021900200104693.","journal-title":"J Appl Probabil"},{"key":"3060_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.ijforecast.2003.09.015","volume":"20","author":"C Holt","year":"2004","unstructured":"Holt C. Forecasting seasonals and trends by exponential weighted moving averages. Int J Forecast. 2004;20:5\u201310. https:\/\/doi.org\/10.1016\/j.ijforecast.2003.09.015.","journal-title":"Int J Forecast"},{"key":"3060_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.112766","author":"F Wang","year":"2020","unstructured":"Wang F, Xuan Z, Zhen Z, Li K, Wang T, Shi M. A day-ahead pv power forecasting method based on lstm-rnn model and time correlation modification under partial daily pattern prediction framework. Energy Convers Manag. 2020. https:\/\/doi.org\/10.1016\/j.enconman.2020.112766.","journal-title":"Energy Convers Manag"},{"key":"3060_CR30","doi-asserted-by":"publisher","unstructured":"Pomorski P, Gorse D. Improving on the Markov-switching regression model by the use of an adaptive moving average. 2023. https:\/\/doi.org\/10.1007\/978-3-031-23844-4_2.","DOI":"10.1007\/978-3-031-23844-4_2"},{"key":"3060_CR31","doi-asserted-by":"crossref","unstructured":"Aimran A. A comparison between single exponential smoothing (ses), double exponential smoothing (des), holt\u2019s (brown) and adaptive response rate exponential smoothing (arres) techniques in forecasting Malaysia population. Glob J Math Anal 2.https:\/\/doi.org\/10.14419\/gjma.v2i4.3253","DOI":"10.14419\/gjma.v2i4.3253"},{"key":"3060_CR32","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2810","author":"V Alevizakos","year":"2020","unstructured":"Alevizakos V, Chatterjee K, Koukouvinos C. A nonparametric triple exponentially weighted moving average sign control chart. Qual Reliabil Eng. 2020. https:\/\/doi.org\/10.1002\/qre.2810.","journal-title":"Qual Reliabil Eng"},{"key":"3060_CR33","unstructured":"Peleg R, Weiss R, Hoogi A. Leveraging the triple exponential moving average for fast-adaptive moment estimation 2023."},{"key":"3060_CR34","doi-asserted-by":"publisher","first-page":"201909726","DOI":"10.1073\/pnas.1909726116","volume":"117","author":"R Hilborn","year":"2020","unstructured":"Hilborn R, Amoroso R, Anderson C, Baum J, Branch T, Costello C, Moor C, Faraj A, Hively D, Jensen O, Kurota H, Little L, Mace P, Mcclanahan T, Melnychuk M, Minto C, Osio G, Parma A, Pons M, Ye Y. Effective fisheries management instrumental in improving fish stock status. Proc Natl Acad Sci. 2020;117:201909726. https:\/\/doi.org\/10.1073\/pnas.1909726116.","journal-title":"Proc Natl Acad Sci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03060-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03060-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03060-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T16:02:37Z","timestamp":1720195357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03060-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,5]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3060"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03060-4","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,5]]},"assertion":[{"value":"30 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"We have obtained informed consent from all individuals who participated in the study described in this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"We grant the right to reproduce, distribute, and publicly display the manuscript, including any figures or supplementary materials.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"704"}}