Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
- Koutroumanidis, Theodoros & Ioannou, Konstantinos & Arabatzis, Garyfallos, 2009. "Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model," Energy Policy, Elsevier, vol. 37(9), pages 3627-3634, September.
- Sotirios Bersimis & Stavros Degiannakis & Dimitrios Georgakellos, 2017.
"Real-time monitoring of carbon monoxide using value-at-risk measure and control charting,"
Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 89-108, January.
- Bersimis, Sotirios & Degiannakis, Stavros & Georgakellos, Dimitrios, 2015. "Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting," MPRA Paper 65865, University Library of Munich, Germany.
- Florin Dan PIELEANU, 2016. "Comparative Study In Estimating Volkswagen’S Price: Arima Versus Ann," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(2), pages 98-109, February.
- Wang, Tai-Yue & Huang, Chien-Yu, 2007. "Improving forecasting performance by employing the Taguchi method," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1052-1065, January.
- Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
- Florin Dan Pieleanu, 2016. "Predicting The Evolution Of Bet Index, Using An Arima Model," Romanian Economic Business Review, Romanian-American University, vol. 10(1), pages 151-162, May.
- Dong-jun Liu & Li Li, 2015. "Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM 2.5 Concentration in Guangzhou, China," IJERPH, MDPI, vol. 12(6), pages 1-15, June.
- Marques, Alex & Lacerda, Daniel Pacheco & Camargo, Luís Felipe Riehs & Teixeira, Rafael, 2014. "Exploring the relationship between marketing and operations: Neural network analysis of marketing decision impacts on delivery performance," International Journal of Production Economics, Elsevier, vol. 153(C), pages 178-190.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
- Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
- Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
- Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
- Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
- Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
- C. Orsenigo & C. Vercellis, 2018. "Anthropogenic influence on global warming for effective cost-benefit analysis: a machine learning perspective," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 425-442, September.
- Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
- Oscar Claveria & Enric Monte & Salvador Torra, 2015.
"“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”,"
AQR Working Papers
201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
- Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
- Amiri, Arshia & Zibaei, Mansour, 2012. "Granger causality between energy use and economic growth in France with using geostatistical models," MPRA Paper 36357, University Library of Munich, Germany.
- Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
- Welch, Eric & Bretschneider, Stuart & Rohrbaugh, John, 1998. "Accuracy of judgmental extrapolation of time series data: Characteristics, causes, and remediation strategies for forecasting," International Journal of Forecasting, Elsevier, vol. 14(1), pages 95-110, March.
- Oscar Claveria & Salvador Torra, 2013.
"“Forecasting Business surveys indicators: neural networks vs. time series models”,"
IREA Working Papers
201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
- Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
- Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
- Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
- Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
- Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
- Sümeyye Çelik, 2020. "Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 261-274, December.
- Eleimon Gonis & Salima Paul & Jon Tucker, 2012. "Rating or no rating? That is the question: an empirical examination of UK companies," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 709-735, September.
- Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:122:y:2000:i:1:p:31-40. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.