Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

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dc.contributor.authorVelumani, P.
dc.contributor.authorNampoothiri, N.V.N.
dc.contributor.statusnemhu_HU
dc.date.accessioned2021-04-01T06:54:39Z
dc.date.available2021-04-01T06:54:39Z
dc.date.issued2021-03-20
dc.description.abstractThe Construction Industry Development Council (CIDC) of India has been calculating and publishing the Construction Cost Index (CCI), monthly, since 1998. Construction cost variations interrogate different kinds of projects such as roads, power plants, buildings, industrial structures, railways and bridges. The success rate of completion of construction project is diminished due to the lack of prediction knowledge in CCI. Predicting CCI in greater accuracy is quite difficult for contractor and academicians. The following factors are influenced higher in CCI such as population, unemployment rate, consumer price index (CPI), long term interest rate, domestic credit growth, Gross Domestic Product (GDP) and money supply (M4). CCI can be used to forecast the construction cost. The relevant resource data was collected across the nation between 2003 and 2018. As outcome-based, non-econometric tools such as smoothing techniques, artificial neural network (ANN) and support vector machines (SVMs) have produced a better outcome. Among these, smoothing techniques have given the notable low error and high accuracy. This accuracy has measured by Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The major objective of this research is to help the cost estimator to avoid underestimation and overestimation.hu_HU
dc.identifier.doi10.1556/1848.2020.00132hu_HU
dc.identifier.issn2062-0810
dc.identifier.issue1hu_HU
dc.identifier.jtitleInternational Review of Applied Sciences and Engineering
dc.identifier.urihttp://hdl.handle.net/2437/305051
dc.identifier.urlhttps://akjournals.com/view/journals/1848/12/1/article-p50.xmlhu_HU
dc.identifier.volume12hu_HU
dc.language.isoenhu_HU
dc.publisherAkadémiai Kiadóhu_HU
dc.subjectConstruction Industry Development Councilhu_HU
dc.subjectConstruction Cost Indexhu_HU
dc.subjectsmoothing techniqueshu_HU
dc.subjectartificial neural networkhu_HU
dc.subjectsupport vector machinehu_HU
dc.subjectpredictionhu_HU
dc.titleVolatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learninghu_HU
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International Review of Applied Sciences and Engineering