Journal ArticleDOI
Improving Accuracy in Predicting City-Level Construction Cost Indices by Combining Linear ARIMA and Nonlinear ANNs
TLDR
In this article , the authors proposed a hybrid ARIMA-ANN model for forecasting construction costs and explored whether the hybrid model can provide more accurate forecasts than an individual ARAMA or ANN.Abstract:
Accurate cost forecasting in budget planning and contract bidding is crucial for the success of construction projects. Linear models such as the autoregressive integrated moving average (ARIMA) and nonlinear models such as the artificial neural network (ANN) have been adopted in the literature for forecasting construction costs. However, both linear and nonlinear models are subject to some limitations derived from their modeling structure and assumptions. This study proposes a hybrid ARIMA-ANN model for forecasting construction costs and explores whether the hybrid ARIMA-ANN model can provide more accurate forecasts than an individual ARIMA or ANN. The national and city-level construction cost indices (CCIs) are forecasted for three forecasting horizons (short-term, mid-term, and long-term) using three forecasting models: (1) linear ARIMA, (2) nonlinear ANNs, and (3) the hybrid ARIMA-ANN model. Out-of-sample forecasting exercise reveals that the hybrid model combining the distinctive features of both ARIMA and ANNs performs better than individual models in most forecasting cases, especially for longer-term forecasting horizons. The findings can help project planners, cost engineers, and decision makers prepare for more accurate budgets and bids for diverse construction projects in different locations. read more
Citations
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Journal ArticleDOI
Characterizing Relationship between Demand Surge and Post-Disaster Reconstruction Capacity Considering Poverty Rates
Sooin Kim,Mohsen Shahandashti +1 more
Proceedings ArticleDOI
Diagnosing and Quantifying Post-Disaster Pipe Material Cost Fluctuations
TL;DR: In this article , the authors investigated the post-disaster fluctuations in pipe costs for a timely reconstruction of pipeline networks and found that the disaster triggered statistically significant increases in pipe cost, including corrugated steel pipe costs, polyvinyl-chloride (PVC) pipe costs and ductile-iron pipe costs.
Journal ArticleDOI
Performance Analysis of Construction Cost Prediction Using Neural Network for Multioutput Regression
TL;DR: In this paper , a multi-output regression model was used to predict seven sub-construction costs using a multiscale regression model, not by predicting a single total construction cost.
Journal ArticleDOI
Actuarial Credibility Approach in Adjusting Initial Cost Estimates of Transport Infrastructure Projects
TL;DR: In this article , a modified actuarial credibility approach is proposed for the adjustment of initial cost estimates of public infrastructure projects by accounting for the additional risk/uncertainty factor, which offers an interesting alternative to other existing forecasting methods.
Journal ArticleDOI
Characterizing relationship between demand surge and post-disaster reconstruction capacity considering poverty rates
TL;DR: In this article , the authors investigated the relationship between labor wage fluctuation and change in building permits of various communities considering their poverty rates and found that demand surge has a statistically significant negative relationship with the reconstruction capacity of counties with above-average poverty rates.
References
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Journal ArticleDOI
Time series forecasting using a hybrid ARIMA and neural network model
TL;DR: Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
Journal ArticleDOI
Tests of Conditional Predictive Ability
TL;DR: This paper proposed a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodelled heterogeneity, incorrect functional form, or any combination of these.
Journal ArticleDOI
Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir
TL;DR: Inflow of the dam reservoir in the 12 past months shows that ARIMA model had a less error compared with the ARMA model, and dynamic artificial neural network model was chosen as the best model for forecasting inflow of the Dez dam reservoir.
Journal ArticleDOI
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting
Mehdi Khashei,Mehdi Bijari +1 more
TL;DR: Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid models and also either of the components models used separately.
Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture
Saurabh Karsoliya,Maulana Azad +1 more
TL;DR: An survey is made in order to resolved the problem of number of neurons in each hidden layer and the number of hidden layers required.
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