scholarly journals Construction Cost Predication Model Using Macro Economic Indicators

10.29007/tjbv ◽  
2020 ◽  
Author(s):  
Craig Capano ◽  
Jeanette Hariharan ◽  
Hashem Moud ◽  
Ashish Asutosh
Keyword(s):  
Time Series ◽  
Construction Industry ◽  
Time Use ◽  
Prediction Models ◽  
Construction Cost ◽  
Economic Indicators ◽  
Economic Data ◽  
Historic Data ◽  
Nonlinear Autoregressive ◽  
Over Time

Estimating future costs of construction is an important component to the success of any contracting company. Traditionally a cost modifier has been utilized to offset cost escalations or volatility predictions. Construction estimators and contractors have also attempted to utilize a variety of prediction models. This paper establishes a basis for reliable forecasting and explores the possibility of developing prediction models using time series Neural Networks (NN) by utilizing historic data of three accepted macro-economic composite indicators (MEI) and two accepted construction industry cost indices. The use of these macro-economic indicators for NN-based models may be used to predict cost escalations for construction. Nonlinear autoregressive NN models are constructed through using the macro-economic data and the construction cost data to determine if a reliable time-series predictive model could be established. The results of these models indicated that there is a high correlation between the macro-economic escalations, independent factors, and the construction cost escalations, dependent factors, over time. Use and knowledge of these correlations could aid in the prediction of cost escalations during construction.

scholarly journals Has the Real-Time Reliability of Monthly Indicators Changed over Time?

2019 ◽  
pp. 1-7
Author(s):  
Mark Bognanni
Keyword(s):  
Time Series ◽  
Real Time ◽  
Economic Data ◽  
Short Term ◽  
Monthly Data ◽  
The Real ◽  
Data Release ◽  
Macroeconomic Indicators ◽  
Over Time

Economic data are routinely revised after they are initially released. I examine the extent to which the real-time reliability of six monthly macroeconomic indicators important to policymakers has remained stable over time by studying the time-series properties of their short-term and long-term revisions. I show that the revisions to many monthly economic indicators display systematic behaviors that policymakers could build into their real-time assessments. I also find that some indicators’ revision series have varied substantially over time, suggesting that these indicators may now be less useful in real time than they once were. Lastly, I find that substantial revisions tend to occur indefinitely after the initial data release, a result which suggests a certain degree of caution is in order when using even thrice-revised monthly data in policymaking.

scholarly journals Possibilities in the application of machine learning on bioimpedance time-series

2019 ◽  
Vol 10 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Christian Tronstad ◽  
Runar Strand-Amundsen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Prediction Models ◽  
Short Term Memory ◽  
Synthetic Data ◽  
Relevant Information ◽  
Published Data ◽  
Input Variables ◽  
Training Examples ◽  
Over Time

Abstract The relation between a biological process and the changes in passive electrical properties of the tissue is often non-linear, in which developing prediction models based on bioimpedance spectra is not trivial. Relevant information on tissue status may also lie in characteristic developments in the bioimpedance spectra over time, often neglected by conventional methods. The aim of this study was to explore possibilities in machine learning methods for time series of bioimpedance spectra, where we used organ ischemia as an example. Based on published data on the development of the bioimpedance spectrum during liver ischemia, a simulation model was made and used to generate sets of synthetic data with different levels of organ-to-organ variation, measurement noise and drift. Three types of artificial neural networks were employed in learning to predict the ischemic duration, based on the simulated datasets. The simulated prediction performance was very dependent on the amount of training examples, the organ-to-organ variation and the selection of input variables from the bioimpedance spectrum. The performance was also affected by noise and drift in the measurement, but a recurrent neural network with long short-term memory units could obtain good predictions even on noisy and drifting measurements. This approach may be relevant for further exploration on several applications of bioimpedance having the purpose of predicting a biological state based on spectra measured over time.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

scholarly journals Creating relationship continuity across projects in the construction industry

IMP Journal ◽  
2017 ◽  
Vol 11 (2) ◽  
pp. 207-229 ◽  
Author(s):  
Malena Ingemansson Havenvid ◽  
Elsebeth Holmen ◽  
Åse Linné ◽  
Ann-Charlott Pedersen
Keyword(s):  
Construction Industry ◽  
Empirical Studies ◽  
Mutual Orientation ◽  
Content Type ◽  
Construction Firms ◽  
Relationship Continuity ◽  
Time Period ◽  
Strategy Types ◽  
The Relationship ◽  
Over Time

Purpose The purpose of this paper is to investigate the relationship continuity across projects among actors in the construction industry, and to discuss why and how such continuity takes place. Design/methodology/approach The authors draw on the results from four in-depth case studies illustrating different strategies for pursuing relationship continuity. The results are analysed and discussed in light of the oft-mentioned strategies suggested by Mintzberg (1987): emergent, deliberate and deliberately emergent strategies. Furthermore, the ARA-model is used to discuss why the relationship continuity strategies are pursued, and which factors might enable and constrain the relationship continuity. Findings The main findings are twofold. First, the authors found that the strategy applied for pursuing relationship continuity may, in one-time period, contain one type of strategy or a mix of strategy types. Second, the type of strategy may evolve over time, from one type of strategy being more pronounced in one period, to other strategies being more pronounced in later periods. The strategies applied by construction firms and their counterparts can thus contain elements of emergent, deliberate and deliberately emergent strategies, in varying degrees over time. It is also shown that the strategies of the involved actors co-evolve as a result of interaction. Also, the main reasons for pursuing continuity appear to lie in the re-use and development of important resources and activities across projects to create efficiency and the possibility to develop mutual orientation, commitment and trust over time, and thus reduce uncertainty. Research limitations/implications Further empirical studies are needed to support the findings. For managers, the main implication is that relationship continuity can arise as part of an emerging interaction pattern between firms or as part of a planned strategy, but that elements of both might be needed to sustain it. Originality/value The authors combine Mintzberg’s strategy concepts with the ARA-model to bring new light to the widely debated issue of discontinuity and fragmentation in the construction industry.

Following the infection dynamics of the tropical trematode Oligogonotylus mayae in its intermediate and definitive hosts for 13 years

2020 ◽  
Vol 94 ◽  
Author(s):  
A.L. May-Tec ◽  
N.A. Herrera-Castillo ◽  
V.M. Vidal-Martínez ◽  
M.L. Aguirre-Macedo
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Southern Oscillation ◽  
Fish Host ◽  
Snail Host ◽  
Monthly Data ◽  
Infection Dynamics ◽  
Infection Parameters ◽  
Over Time

Abstract We present a time series of 13 years (2003–2016) of continuous monthly data on the prevalence and mean abundance of the trematode Oligogonotylus mayae for all the hosts involved in its life cycle. We aimed to determine whether annual (or longer than annual) environmental fluctuations affect these infection parameters of O. mayae in its intermediate snail host Pyrgophorus coronatus, and its second and definitive fish host Mayaheros urophthalmus from the Celestun tropical coastal lagoon, Yucatan, Mexico. Fourier time series analysis was used to identify infection peaks over time, and cross-correlation among environmental forcings and infection parameters. Our results suggest that the transmission of O. mayae in all its hosts was influenced by the annual patterns of temperature, salinity and rainfall. However, there was a biannual accumulation of metacercarial stages of O. mayae in M. urophthalmus, apparently associated with the temporal range of the El Niño-Southern Oscillation (five years) and the recovery of the trematode population after a devasting hurricane. Taking O. mayae as an example of what could be happening to other trematodes, it is becoming clear that environmental forcings acting at long-term temporal scales affect the population dynamics of these parasites.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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